Ontology-Driven Generic Questionnaire Design. Maryam Alipour-Aghdam. A Thesis presented to The University of Guelph

Transcription

1 Ontology-Driven Generic Questionnaire Design by Maryam Alipour-Aghdam A Thesis presented to The University of Guelph In partial fulfillment of requirements for the degree of Master of Science in Computer Science Guelph, Ontario, Canada c Maryam Alipour-Aghdam, August, 2014

2 ABSTRACT Ontology-Driven Generic Questionnaire Design Maryam Alipour-Aghdam University of Guelph, 2014 Advisor: Dr. D.A. Stacey Computer based questionnaire has an important role in collecting data in different domain. As the use of computer based questionnaires have been increased in different domains, each of them uses different technique to create and design the questionnaire. The main tools that have been used to create questionnaire in different domains mostly have the lack of semantic web, generic structure, and consist of large amount of hard coding. This thesis has three major purposes: (1) to create generic questionnaire ontology to facilitate computer based questionnaire design that can be used in various domains. (2) to merge different domain ontologies with the questionnaire ontology that can be used as an error checking mechanism during questionnaire creation (3) to modify the questionnaire content when the domain ontology content have been modified. Three proposed prototype systems have been designed and implemented in this study to demonstrate these purposes.

3 I would like to dedicate this thesis to my parents, my husband, Manouchehr, and my beloved son, Kiarash for their constant support and unconditional love. I love you all dearly. iii

4 Acknowledgments Pursuing my Master was one of the best valuable experiences I have ever had in my life. This achievement would not have been possible without the help, support, and inspiration of many people around me. I would like to express my sincere appreciation and gratitude to my supervisor Prof. Deborah Stacey. Her valuable knowledge, experience, comments, suggestions, and encouragement have always motivated me to do better. I could not have completed this thesis without her support. I would like to thank the members of my committee Dr. David Calvert and Dr. Daniel Gillis for serving on my thesis committee. I feel a deep gratitude to my parents, Shahla Ashktorab and Hassan Alipour-Aghdam, who have sacrificed so much to provide me with the quality of life from which I could choose what I wanted to be or to do. Even though we are thousands of miles away, you were always there whenever I needed you. I would like to thank my brother, Amir, as well as my sister, Maral, for their constant encouragement. I thank my son Kiarash; you are the light of my life. I can only hope to bring you as much joy as you have given me. I owe you lots and lots of fun hours. You really gave me the reason to continue. Special thanks are due to my husband and my best friend, Manouchehr, without whose love, encouragement and support, I would not have finished this thesis. He has cherished with me every great moment and supported me whenever I needed it. iv

5 Table of Contents List of Figures vii 1 Introduction Motivation Thesis Statement Anatomy of a Questionnaire Questions Branching and Logic Organization of the Thesis Background and Literature Review Data Collection and Surveys Semantic Web and Ontology Introduction to Ontologies Web Ontology Language (OWL) Ontology Reasoner Semantic Web Rule Language Ontologies in Surveys Why Should We Use Ontologies? How Have Ontologies Been Used in Surveys? Our Approach and the Proposed Prototype Creating A Generic Questionnaire Ontology Why We Use An Ontology to Create Questionnaires Proposed Prototype Proposed Application Creating Questionnaire Ontology by Using Domain Ontology for Error Checking Purposes Proposed Prototype Proposed Application v

6 3.3 Modifying the Questionnaire when the Domain Ontology Content Has Been Modified Proposed Prototype and Application Case Studies Background and Purposes Case Study1: Creating a Questionnaire Without Using a Domain Ontology Case Study 2: Creating a Questionnaire Using a Domain Ontology Case Study 3: Editing the Questionnaire When the Domain Ontology is Modified Conclusions and Future Work Conclusions System Properties and Limitations Scalability Robustness Verification Future Works Framework Questionnaire Ontology Merging Questionnaire Ontology with Domain Ontology Questionnaire Structure Appendix A Overview of the ontologies Overview of the Generic Questionnaire Ontology Bibliography 76 vi

7 List of Figures 1.1 Multiple Choice Single Selection Question Multiple Choice Multiple Selection Question Likert Scale Question Balanced Scale Question Dichotomous Question Semantic Differential Question Open Ended Question Questionnaire Ontology Structure Answer Set Class Subclasses Structure Answer Set Class Subclasses Structure Label Class Subclasses Structure Added Ontology Structure to Keep Domain Ontology Contents Proposed Application Steps Proposed Prototype and Application Steps First Scenario Questionnaire Number of Question will be Asked by Application Question Text will be Asked by Application Application Steps in Creating a Questionnaire Creation of Questions and Their Components in Ontology Creation of Question Type Individual in the Ontology Creation of Answer Individual in the Ontology Creation of Answer Label Individual in the Ontology Domain Ontology Data Properties SWRL Rules Created in the Ontology Application Steps in Creating a Questionnaire Using a Domain Ontology Domain Ontology Imported into the Questionnaire Ontology Creation of Question Individual into Ontology in case of Using Domain Ontology Creation of Domain Ontology Individual by Merging the Domain Ontology into the Questionnaire Ontology vii

8 4.15 Creation of Question Type Individual in Ontology Creation of Answer Individual in the Ontology Creation of Answer Label Individual in the Ontology Data Properties of Modified Domain Ontology Application Steps in Editing a Questionnaire using a New Version of the Domain Ontology SWRL Rules Created based on the Newest Version of the Domain Ontology Modified Answer Label based on Using Newest Revision of the Domain Ontology Future Work Structure Overview of the Thing Class Hierarchy Overview of the AnswerSet Class Hierarchy Overview of the Answer Class Hierarchy Overview of the Label Class Hierarchy Overview of the Question Class Description Overview of the Question Class Description Overview of the Generic Questionnaire Ontology Structure viii

9 Chapter 1 Introduction 1.1 Motivation A questionnaire is a set of questions created in a written format[14]. Questionnaires aim at extracting specific information from some sample of individuals. They are used as a data collection method. Data collection is used to collect precise and dependable information when producing a field study about the pertinent topics[14]. Questionnaires are straightforward instruments of data collection. They are centered in the asking of a series of questions. They can be in a close-ended or restricted form, i.e. multiple choice, or they can be in an open-ended or unrestricted form, i.e. fill in the blanks[14]. Questionnaires can be distributed quickly and easily so they are the most popular data collection method. There are some advantages and disadvantages to questionnaires. Questionnaires are time and cost effective. They can be filled out by respondents when they have time. Researchers do not need to schedule a session with respondents to fill out the questionnaire. Moreover, questionnaires as a data collection technique are flexible as they can capture a wide range of cognitive and behavioural phenomena[20]. On the other hand, as questionnaires can be answered by respondents without interviewers present, vague questions are problematic[14]. In addition, question responses are based on the respondent s way of thinking and their behaviour and that is hard to capture even with an interviewer. In addition, the data 1

10 collected using questionnaires can be subjective and at best give an imperfect picture[20]. Regardless of the pros and cons of questionnaires, among all the data collection techniques, questionnaires are by far the most famous. Ton-That et al.[20] mentioned that 75 primary studies out of 101 use questionnaires in their research methodology. 1.2 Thesis Statement Computer-based questionnaires have an important role in collecting data in different domains such as health care and business. The data that is collected by this method have added value compared to pen and paper questionnaires. However, the data collected in different questionnaires may not be stored as the same data type and thus can create difficulty in the analysis step. As the use of computer-based questionnaires is increased in different domains to help in collecting data from clients, each of them uses different techniques to create and design the questionnaire. This mostly consists of a large amount of hard coding. For further changes inside their questionnaires developers must also make a large number of changes in the application and this is time consuming. In addition, no generic questionnaire exists that has available sources so each different domain can create changes to its design by their developers. Ontologies can be used in facilitating computer based questionnaire design by creating a generic questionnaire ontology that can be used in different domains. The ontology driven generic questionnaire can improve the collection of data and the design of computer-based questionnaires can be made easier and faster compared to the hard coding of questionnaire software. As a result of ontology specification, the ontology can be shared and especially reused in different domains. A generic computer-based questionnaire ontology can be used in different domains and merged with different domains specific ontology to be used for error checking to facilitate the creation of domain specific questionnaires. 2

11 In this research, we will prove that an ontology-driven generic questionnaire design can be created to facilitate computer-based questionnaires that can be used in different domains to collect data. In addition, we will show that using ontologies to create questionnaires will enhance data collection and analysis and will make adding and editing a new type of question more convenient and less dependent on hard coding. Furthermore, we will answer the question as to whether different domain ontologies can be merged with a questionnaire ontology and then used as an error checking mechanism during questionnaire creation. Moreover, creating a questionnaire by using ontologies has the advantage that a questionnaire structure restriction can be implemented in the ontology and reasoning can then be done by using an ontology reasoner instead of creating a hard coded reasoner in an application. Ontologies will be used to facilitate computer based questionnaire by defining the structure of questionnaire in the ontology. By defining the structure of questionnaire in the ontology, minimal hard coding will be done in the application. Since changes would be happen mostly in the ontology and further modification of questionnaire would be easier and faster. In addition, hard coded reasoner that was needed to be implemented before would be replaced by ontology reasoner as ontology has its own reasoner to create accurate sets of questions. In this work, we will prove that questionnaire ontology will be used in different domains by defining the questionnaire structure in the ontology in a generic format. As a result, it will not contain the content of data from any specific domain and will be used to create a questionnaire in different domains. Furthermore, we will demonstrate that different domain ontologies can be merged to questionnaire ontology to be used as an error checking mechanism in questionnaire creation. As a result, different domain ontologies will be imported to questionnaire ontology and Semantic Web Rule Language will be used to create a combined rule of questionnaire ontology and domain ontology to be used in error checking mechanism. 3

12 1.3 Anatomy of a Questionnaire Questionnaires are common data collection tools that have been used in different domains. The structure of questionnaires consists of a group of questions and answers that will be created with the goal of data collection. In the following sections, we will define questions, answers, and branching and logic that have been used in questionnaires Questions In this section, we focus on different types of questions that are created in different domains regardless of the content of the domain. The main types of questions that have been used in different domains and different types of questionnaires are the following types: Multiple Choice Single Selection Multiple Choice Multiple Selection Likert Scale Balanced Scale Dichotomous Semantic Differential Open Ended Multiple Choice Single Selection Multiple Choice Single Selection questions are multiple-choice questions that ask users to select one answer choice from a list of choices. Figure 1.1 on page 5 is an example of a Multiple Choice Single Selection question that could be used in a questionnaire. 4

13 Figure 1.1: Multiple Choice Single Selection Question Multiple Choice Multiple Selections Multiple Choice Multiple Selections questions are questions that ask users to select one or more answer choices from a list of choices. A question may or may not specify the number of choices to select. Figure 1.2 on page 5 is an example of a Multiple Choice Multiple Selections question that could be used in a questionnaire. Figure 1.2: Multiple Choice Multiple Selection Question 5

14 Likert Scale Likert Scale questions are mostly used to scale responses in survey research. Likert Scale questions are most widely used to measure someone s attitude and behaviour by answer choices that range from one extreme to another (for example, Strongly Agree to Strongly Disagree[19]. Likert Scale questions are used to reveal degrees of opinion. The most common answer choices which are used for Likert Scale questions are agreement (for example, Strongly Agree to Strongly Disagree), frequency (for example, Very Frequently to Never), importance (for example, Very Important to Never), quality (for example, Very Good to Very Poor), and likelihood (for example, Almost Always True to Almost Never True). Likert Scale questions are given scores or assigned weight to each answer choices for each question. The purpose of Likert Scale questions are to collect respondents scores to find average ranking for each answer choice in order to define overall which answer choice was the highest ranked. Figure 1.3 on page 6 is an example of a Likert Scale question that could be used in a questionnaire. Figure 1.3: Likert Scale Question Balanced Scale Balanced Scale questions are considered to be rating scale questions. In Balanced Scale questions, the number of positive and negative statements for each rating points are equal. 6

15 In addition, to positive and negative statements for a Balanced Scale question, a neutral midpoint is also needed to balance the scales in this type of question. Figure 1.4 on page 7 is an example of Balanced Scale question that could be used in a questionnaire. Figure 1.4: Balanced Scale Question Dichotomous Dichotomous questions are questions which allow respondents to choose one of two answer choices. Common answer choices that are used for Dichotomous questions are Yes and No and True and False. Figure 1.5 on page 7 is an example of a Dichotomous question that could be used in a questionnaire. Figure 1.5: Dichotomous Question Semantic Differential Semantic Differential questions are considered to be rating scale questions. The question type does not label each rating point with an individual descriptive like Likert Scale questions. In Semantic Differential questions, two statements are placed one at the far left and the opposite 7

16 statement is placed at the far right. In this question type, a numbering system is used within the scale and the respondents will choose a number between the two statements[19]. Figure 1.6 on page 8 is an example of Semantic Differential question that could be used in a questionnaire. Figure 1.6: Semantic Differential Question Open Ended Open Ended questions require respondents to type their answers in the answer boxes and do not define specific pre-set answers for questions. Figure 1.7 on page 8 is an example of Open Ended question that could be used in a questionnaire. Figure 1.7: Open Ended Question 8

17 1.3.2 Branching and Logic Regardless of the type of questions previously discussed, a questionnaire includes a series of questions and answers as their components. Conditional branching is a feature that can be used in questionnaire design. Conditional branching is part of content adaptation features that can be used in questionnaire design[3]. By using conditional branching in questionnaires, different respondents will receive different questions depending on the answers to earlier questions[4] Organization of the Thesis The Chapter that follows discusses two fields of interest: semantic web and questionnaires. It explores the characteristics of these fields and technologies that have been used in these areas. It also looks at the suitability of the semantic web for creating a generic questionnaire and for developing an error checking method in the questionnaire. Chapter 3 outlines the approach followed in the development of our prototype system. It discusses the components of this system including ontologies, SWRL rules, reasoner, and platforms that have been created. Case studies that demonstrates aspects of this system are discussed in Chapter 4. We conclude this thesis by presenting our conclusions and future work in Chapter 5. 9

18 Chapter 2 Background and Literature Review 2.1 Data Collection and Surveys Surveys and questionnaires are used as a method of information collection in systems in different domains. The data that is collected through questionnaires will be analyzed and used based on the goal of the questionnaires creators. Different types of questionnaires exist that can mainly be categorized into pen and paper questionnaires and computer-based questionnaires. There are some benefits/drawbacks of computer-based questionnaires versus pen and paper questionnaires. Researchers have been interested in electronic questionnaires, for both academic and commercial aspects, due to the potential of electronic questionnaires to decrease the cost of surveys[4]. Computer-based questionnaires as a form of data collection have advantages compared to pen and paper questionnaires since they are less time-consuming and more efficient[3]. In terms of efficiency, in electronic questionnaires, data entry is automated and this will create greater data accuracy and greater efficiency[4]. They can be considered as a method of data collection system that offer considerable cost advantages compare to print questionnaires[4]. Electronic questionnaires lead to more complete data and therefore have a higher completion rate compared to print questionnaires[4]. The benefits of using computer-based questionnaires for researchers who are facing problems of missing data is 10

19 that they have fewer missing responses. In addition, some of the mechanical benefits of using computer-based questionnaires are the capability to add more detailed descriptions about questions and form more precise questions[4]. Moreover, one can create a more interesting questionnaire for the respondents by adding pictures and colour formatting to questionnaires. However, in general, for printed questionnaires less time and effort has to be considered in the development of printed questionnaires[4]. One of the main advantages of online questionnaires is their ability to promptly analyze the collected data to find out if the trend in data is satisfactory or not and if any changes to the collection has to be made during research[11]. Therefore, having this information available only at the end of the research can be costly and as a result, pen and paper questionnaires can generate more cost in terms of data collection compare to computer-based questionnaires. In terms of the medical domain, using computer-based questionnaires has several advantages. They can help clinicians focus more on medical care instead of filling in a patient s pen and paper questionnaires. Multiple languages can be supported and multimedia support can be used for special needs patients[3]. In addition, the information collected through using computer-based questionnaire is more structured and detailed compared to other methods of data collection[3]. Different computer-based questionnaires have been created to collect data to be analyzed and used in different domains. Most computer based questionnaires are hard coded applications that take a large amount of effort from developers to make changes to questionnaires and extend them. In addition, hard coded application questionnaires cannot be shared and reused easily. Different research has been done in this area. Bouamrane et al.[3] pro- 11

20 posed a Computer-based Information Collection System (ICS) to collect more accurate data, generating time saving and lower cost compare to what was collected in pen and paper questionnaires and face-to-face interviews. Computer-based ICSs also propose using multiple languages in questionnaires and helping people with special needs by using multimedia support. Ginneken et al.[21] proposed a generic computer-based questionnaire application, to separate functionality and database structure from content in order to provide a computerbased functionality that is not depend on the content. The system was proposed as an addition to OpenSDE that is a generic application for structured data entry. In this work the proposed computer-based questionnaire application can be used as an alternative way for data input. A domain model is used as a base for data entry in the form of a tree. It has a hierarchical structure but does not include a reasoner. The computer based editor for OpenSDE is used to create questionnaires for a variety of medical domains. Although they were looking to offer a generic questionnaire construction with less effort and high flexibility, they still need to put considerable programming effort into creating restrictions for different types of questions. 2.2 Semantic Web and Ontology The Semantic Web has been developing in recent years to facilitate the difficulty that exists in shared resources communication. The Semantic Web is an extension of the World Wide Web that make sharing the content possible for people without using applications and websites[12]. The semantic web tries to make the meaning precise and automatic correlation feasible. According to the World Wide Web Consortium(W3C)[7] using the Semantic Web will allow the data be processed automatically by tools and manually. It is an extension of the web where information is given well defined meanings to help people and computers work in collaboration[7]. An example of a Semantic Web application is an automated travel agent 12

21 that offers suitable vacations or travel suggestions, based on a user s given preferences[12]. In this case, the Semantic Web application will search the web to find the related information in the same way that a user planning for a vacation would[12]. The difficulty that will is faced in terms of sharing and processing web content is that much of the web content is unstructured[12]. In order to overcome this difficulty, the meaning of new terms has to be defined by combining or restricting existing terms on the web. Therefore, ontologies has been used to provide an extendable vocabulary of terms with well-defined meaning[12]. The main tool that proposes unification that will create data and metadata compilation by using structured knowledge is ontologies. The term ontology in computer science is the description of a conceptual domain by using a formal data structure. The data structure generally contains of a set of statements that defines concepts and the relationships between concepts. A concept is the statement of an entity that uses properties to define its actual meaning Introduction to Ontologies An ontology is defined as a formal, explicit specification of a shared conceptualization. It represents a specific vocabulary used to express a certain reality[8]. Ontologies are suggested to authorize both conceptual models and schemas[8]. They have been developed to facilitate knowledge reuse and sharing by the Artificial Intelligence community. Ontologies are mostly used to describe domain knowledge[6]. They propose a formal specification of the vocabularies that are included in domain concepts and create the relationship between those vocabularies in a domain of interest[9]. A data standardization and conceptualization through a machine understandable ontology language is a general use of ontologies[6]. Examples of real world ontologies include the catalog on the Web such as Yahoo! categories, 13

22 taxonomies for online shopping such as Amazon.com s product catalog and domain specific terminologies such as the SWEET and GENE ontologies[6]. In order to define ontologies, different ontology languages have been created. In 2001, W3C set up a working group to develop a standard for web ontology language as a necessary tool for the development of the semantic web[12]. In 2004, the outcome was Web Ontology Language (OWL) Web Ontology Language (OWL) An ontology language is the formal language to build a knowledge model or ontology. The Web Ontology Language is one of the most recent modulus ontology languages that is certified by W3C[1]. OWL is used to process the content of information instead of just presenting the information to humans. It proposed machine with a higher explainable level that introduced extra vocabulary along with a semantic compare to former ontology languages[15]. OWL can be used to express the meaning of terms in a vocabulary and the relationships between those terms. Moreover, OWL adds more vocabulary to express properties and classes, the relationships between classes, the typing of properties, the characteristic of properties and much more. OWL model knowledge includes C lasses, P roperties, I ndividuals and Restrictions[1]. Reasoning capability has an important and critical role for applications developed for the Semantic Web[18]. The reasoner can be used to check if all the statements and definitions in an ontology are consistent according to the ontology and find out which concepts are categorized under which definitions. In the next section, the role of the reasoner will be defined in ontologies. 14

23 2.2.3 Ontology Reasoner Many applications that have been developed for the Semantic Web need to have reasoning ability. Without having a reasoner in many of the Semantic Web applications, they cannot operate properly[18]. Pellet as an OWL-DL reasoner is a rich feature reasoner that can be used with Web Ontology Language (OWL). It has improvements over previous DL reasoners that lack some specifications which are essentials for the Semantic Web, such as, reasoning with individuals, querying capabilities, etc.[18]. An OWL-DL reasoner proposes a set of services which include consistency checking, classification, and realizations that are defined as follows[1]: Consistency checking: makes sure that the knowledge model is consistent and does not contain any conflicting facts. Classification: Calculates the classes-subclasses relation and creates connections between classes using defined properties to create the entire class hierarchy. Realization: To find out which individuals belong to which specific classes. Pellet proposed programmatic access to the reasoning feature in two interfaces, OWL API library and Jena toolkit[18]. One of the main features of Pellet that gives it an advantage over other DL reasoners is that it supports Semantic Web Rule Language (SWRL) Semantic Web Rule Language The Semantic Web Rule Language (SWRL) is the standard rule language of the Semantic Web. It creates the ability to write Horn Logic-like rules that are expressed and used in OWL concepts[17]. It is used to reason about OWL individuals primarily in terms of OWL classes and properties. SWRL rules give users the ability to write rules to reason about individuals 15

24 and find inferred knowledge about those individuals[1]. In common with other rule languages, SWRL rules are written as antecedent-consequent pairs. In the SWRL language, antecedent refers to the body and consequent refers to the head. Both body and head consist of a positive conjunction of atoms[17]. In a SWRL rule if all the atoms in the body are true then the head also must be true. For instance, in the following example a SWRL rule is created to declare that a person with a male sibling has a brother. Person(?p) hassibling(?p,?s) Man(?s) hasbrother(?p,?s) In this rule Person(?p) hassibling(?p,?s) Man(?s) creates the body or antecedent of the SWRL rule and hasbrother(?p,?s) creates the head or consequent of the SWRL rule. Therefore, in this case, hasbrother(?p,?s) is true when all the atoms in the body are true. 2.3 Ontologies in Surveys Why Should We Use Ontologies? The general goal of using ontologies is to represent and communicate about what we know about the world. One of the main reasons to use ontologies is to create an explicit domain assumption. The ontology simplifies the structure of knowledge and the knowledge representation of the domain. Therefore, to represent an effective knowledge representation system of a domain an effective ontology analysis should be performed in the domain. A fragile ontology analysis will lead to disjointed knowledge bases.[5]. The second main reason to use ontologies is to share the general knowledge representation of a domain among people or software agents who have similar needs for knowledge representation in that domain. Therefore, similar needs for replicating knowledge analysis on that domain would be eliminated. Shared ontologies could be used to create specific knowledge bases that describe specific situations. This type of sharing will enable the reuse of domain knowledge. In contrast with the former 16

25 generation of knowledge representation languages, the use of ontologies will create content rich languages that contain a large number of terms that cover the content theory of that domain [5]. So, the ontology is a long lived conceptual domain that can be used in different applications. In addition, a domain representation in the form of an ontology is more technology neutral. So by using an ontology we can move away from application concerns in contrast with the technology used for constructing computer systems that frequently change [16]. Creating ontologies instead of databases provides a level of abstraction for data models. An ontology presents content at a semantic level while databases present data at a logical or physical level. Ontologies can be used to combine various databases, allowing interoperability among distinct systems. Moreover, ontologies can be used to describe a set of concepts and relationships to represent the content and organization of some subjects in a formal language. However, database schema only explain the organization of a database in a formal language. Data collection is one of the areas that need to use ontologies to propose improvement. An online questionnaire can be considered as a form of data collection that is used in different domains How Have Ontologies Been Used in Surveys? Although research has been done on computer-based questionnaires, there are few studies that have been done on using ontologies for surveys. These studies are mainly focused on specific domains. The medical and health care domain is one of the most active domains in using questionnaires in data collection usually in the gathering of patient data. The use of ontologies is suitable for medicine and biology application as these domains are knowledgeintensive and face an exponential growth of data. 17

26 Huq et al.[13] presented their proposed ontologies for online surveys in the healthcare domain. They proposed a generalized survey authoring tool. In their research, as they were focusing on the healthcare domain, they tried to clarify the different components of different questionnaire tools and define them by existing standards to design ontologies for online questionnaires. The problem that they were trying to address was the physical resource restriction to avoid full time access to data for physicians and caregivers. They proposed an Internet application to collect patient data and to publish patient educational material. The other method that was proposed before this method used HTML to collect patients data through the Internet and was developed for a single survey and was not adaptable to other questionnaires without significant recoding. The proposed ontology in this method tried to overcome the challenges existing in former methods to be able to create different types of surveys without more coding and to allow branching. Although the main focus of the research was on proposing ontologies for questionnaires, it was focused only on medical domains and cannot be considered as a generic questionnaire ontology. Bouamnrane et al.[3] proposed an ontology-driven adaptive medical questionnaire with the goal of being general enough to be used by the majority of patients while capturing at the same time critical information from individual patients. Their generic questionnaire before adaptation had a number of disadvantages that include: patients had to answer a number of fixed question regardless of their situation; the clinicians had incomplete data after gathering data through questionnaires; and from the software developer s point of view any system structure change created significant software engineering work for them [3]. They developed a generic questionnaire ontology that extracted structured elements from a medical questionnaire. The main ontology classes for the system include: Questionnaire, Subques- 18

27 tionnaire, StartOfQuestionnnaire, Question, Answer, and Further Question. The system is implemented using three main components that include user interface, the Java adaptive engine, and the questionnaire ontology. The ontology system presents a compound design of medical questionnaires that can adapt the structure of a questionnaire to user interaction. The focus of the system was on reducing the number of questions while collecting more patients information when required in order to perform a proper risk assessment [2]. The main reason that their work is not generic is because domain content and questionnaire content are created under the same ontology and it is specific to the medical domain. In the first approach, Huq et al. [13] presented ontologies for online questionnaires. In their research, online questionnaires are the composition of questionnaire contents and domain contents. They mainly focused on the healthcare domain to design ontologies for online questionnaire. In this research, I will focus on creating a generic questionnaire ontology that mainly focuses on the content of questionnaires. Later, I will be merging different domain ontologies to be used in creating questionnaires. Therefore, many domains can reuse the generic questionnaire ontology to create a domain specific questionnaire. In the second approach, Bouamnrane et al.[3] presented an ontology-driven adaptive medical information collection system.the ontology is used for dynamic modification of the application s behaviour in response to user interaction. Furthermore, the presented ontology is specified mainly for health domains and cannot be used in other domains. The structure of the ontology is mainly question-centric and focused on content adaptation. In this research, I will work on creating a generic questionnaire ontology that will be answer-centric. I will be focused on answer analysis during the questionnaire creation stage to incorporate domain knowledge in the questionnaire. In order to work on data analysis in my research, I will work 19

28 on creating rules that are answer-centric. 20

29 Chapter 3 Our Approach and the Proposed Prototype In this research, we will use ontologies to create questionnaires and facilitate questionnaire use. A questionnaire contains a group of questions and answers. Each question includes a text that asks respondents a question and a group of answers. The answers will be the choices that respondents can choose depending on the question they have been asked. In general, the questionnaire will define a structure and will be different in terms of structure depending on the question type. Question types have been defined earlier in Chapter 1. For example, if the questionnaire creator selects multiple choice single selection or Likert scale, the structure of the questions will be different. However, questionnaires also have semantic parts that need to be looked at. In a questionnaire, the question text includes contents that can create linkages to a specific domain. This part of a question is the semantic part. Therefore, from the question text, the domain that the question needs to be linked to can be identified. For example, if the question is about health insurance and the term health insurance is used in the question text, the health insurance domain will be linked to the questionnaire. Therefore, in this research we will focus on this semantic aspect of questionnaires using ontologies as semantics are the main and important objective of this domain specification. In this research, we will focus on three main goals and explain our approaches and proposed prototypes in the three sections in this chapter. 21

30 3.1 Creating A Generic Questionnaire Ontology The primary focus of this Thesis is to represent the power of ontologies that are used as the main tool in creating generic questionnaires. By generic questionnaire we mean a questionnaire that does not look at the content of data and is mainly focused on the structure of different types of questions and the type of data and number of selections we can have in the answers. Therefore, creating the generic questionnaire ontology was the first goal of this Thesis, to demonstrate the advantages of using ontologies and a reasoner. Questionnaires have been used in different domains to collect the data from people who are dealing with that domain. The data is then analyzed later to be used in different applications on that domain. Therefore, questionnaires are the common data collection tools that would be used in most domains and are the main focus of many data collections. In the first part of our approach, we focus on different types of questions that are created in different domains regardless of the content of the domains. The main types of questions that have been used in different domains are the following types that had been discussed earlier in Chapter 1: Multiple Choice Single Selection Multiple Choice Multiple Selection Likert Scale Balanced Scale Dichotomous Semantic Differential Open Ended 22

31 In each different type of question the main differences are the number of answers that the question could have and the number of selection that the users can select. They also are different in the way that the data is presented but that is not emphasized in this research. This aspect of questionnaires is generally a function of the choices made by the software developer depending on how they want to present the questionnaire. In some types of questions we also need to give the chance to the user to insert the data as free text if he cannot find the data that needs to be selected. One question that can be asked is why should one choose to use ontologies to create questionnaires when there are different tools on the Internet for creating surveys? In the next section, we will go through the different reasons for why we use an ontology to create questionnaires Why We Use An Ontology to Create Questionnaires The generic questionnaire ontology will be considered as a method for the sharing and reusing of knowledge among software entities. In addition, the main reason to use an ontology for creating a generic questionnaire is to make sure questions are proper by using an ontology reasoner. By using an ontology, the large amount of hard coding that is used in other questionnaire creator tools can be reduced significantly. Reasoners play an important role in creating questionnaires as they can make sure questions are accurate and proper according to the question/answer structure chosen. Using an ontology would facilitate using a reasoner since most ontology language systems have their own associated reasoner and this eliminates the need to develop a reasoner by the developer. The ontology reasoner will use the specifications and restrictions that have been created by questionnaire s creator inside the ontology to help in the creation of questions based on the ontology, The question can be guaranteed to follow the same restrictions and specifications that have been developed and stored inside the ontology. After defining our questionnaire ontology, the Java language has 23

32 been used to create a framework that can be used by users to create and verify questions in a questionnaire. No reasoning is done directly in the Java and the amount of code that had to be written in Java was minimal. The other advantage of using ontologies to create questionnaires is that the person who will work with the ontology to modify or expand it, does not need to have a knowledge of computer programming and only needs to know specific concepts to create parts of the ontology. The prototype framework could be used by a person who has knowledge about creating questionnaires. The ease of use of the prototype framework compares favourably to the time necessary to create and modify questionnaires created using other questionnaire systems that often require coding changes that can only be made by a software developer Proposed Prototype In this research, first we focused on how the generic questionnaire would be created using an ontology. We selected Web Ontology Language (OWL) as our ontology representation language and we used Protégé-OWL, a free open source ontology editor and knowledge base framework, as a knowledge modelling tool to create the knowledge model for the generic questionnaire ontology. OWL is used as the ontology representation language in this research for two main reasons: (1) OWL is designed for sharing the information on the Web therefore it is accessible and can be used with other questionnaire creators or groups who are working on the structure of questionnaires; (2) OWL has a built-in classification inference method [1]. The questionnaire ontology is made up of four main classes: Answer, Answer Set, Label, Question. Figure 3.1 on page 25 shows the structure of the main classes in the questionnaire ontology. A brief description of the main classes in the questionnaire ontology with subclasses and properties is included below. 24

33 Figure 3.1: Questionnaire Ontology Structure Answer Set The first step in creating the questionnaire ontology was to specify the different types of questions and find specifications for each of them. The different types of questions were defined under the class AnswerSet which is a subclass of Thing. Therefore, all different question types that were mentioned before were considered as an answer set under the AnswerSet class. Figure 3.2 on page 26 shows the subclasses of Answer Set class in the questionnaire ontology. In the next step for Answer Set class, we find the similarities and the differences that exist between these question types. The similarities have been used to create common properties that can be used by different types of questions.the common data properties that were found in all different answer sets were the number of answers and the number of selections that are defined by has Number Of Answers and has Number Of Selected. These data properties 25

34 Figure 3.2: Answer Set Class Subclasses Structure are used and assigned for each specific type of question to define the number of answers and the number of selections that can be made. Data Properties: has Number Of Answers, has Number Of Selected Question Question class is one of the main classes in the questionnaire ontology. It is used in the ontology to store the question text which is the semantic content of the question. Question text will be chosen by the questionnaire creator later when creating rich content for the questionnaire. Each Question has an answer set depending on the type of answer set that were defined previously and which type of characteristic the questionnaire creator is looking for. The data property that has been created for this class is created to store the question text. Also different object properties have been used to connect a question to other related classes in the ontology. The object properties listed in this section have Question class as their domains. Data Properties: has Question Text Object Properties: hasanswerset, hasdomainontology, hasitem, hasmergeddomainclass 26

35 Answer The Answer class has been defined in the questionnaire ontology to give the description of answers that will be defined for each type of question. In this class we will store how many answers will be created for each specific question. The Answer class is connected to the Answer Set class with this description: Answer isanswerof some AnswerSet. In addition, Answer class will be used to connect to each specific answer in each question. Data Properties: has Number Of Answer Label Object Properties: haslabel, isanswerof One of the other concepts that has been defined in this work is when we are dealing with open ended questions; it has been categorized to free text and restricted. Restricted open text includes when the user uses the open ended answer to insert, for example, their address, postal code, date, or phone number. Each of these has different patterns that need to be followed by the person who is going to respond to the questionnaire. All of these patterns also have been defined in the ontology so in case the ontology is to be used for analyzing user data, the reasoner can use this pattern to make sure the user inserted the correct data to be used for processing. In order to keep these answers in the questionnaire ontology, an Open Ended class has been defined as a subclass of Answer class. The Open Ended classes also include two subclasses Free Text and Restricted. Figure 3.3 on page 28 shows the structure of Answer class and subclasses that have been defined for this class. Label Label class is one of the main classes in the questionnaire ontology and is used to store the answer contents. Two types of labels that have been defined as a subclass under Label class re Answer Label and Position Label. Figure 3.4 on page 29 shows the structure of Label class in the questionnaire ontology. Answer Label is used to store the information about the content, position and visibility of each answer. Therefore, depending on the number of answers that have been selected by the 27

36 Figure 3.3: Answer Set Class Subclasses Structure questionnaire creator, the same number of answer labels for that specific question will be created. All answers for each specific question will be grouped by using the Answer class as the Answer class has the task of grouping and keeping the connection between each question and the Answer Labels that have been created for each class. The reason to use Answer Label is to capture the information about each label separately. Therefore when the data is going to be reasoned with and analyzed later it can be linked, modified and changed easily. Data Properties: has Position Number, has Label Text, is Visible Object Properties: islabelof Position Label has also been defined as a subclass under Label class to be used with specific question types such as semantic differential questions that have position labels at the beginning and end of each question. 28

37 Figure 3.4: Label Class Subclasses Structure Data Properties: has PositionLabel Text, is Visible Object Properties: HasPosition In conclusion, each Question has an Answer Set depending on the types of Answer Set that have been selected by the questionnaire s creators and the type of characteristics they are exactly looking for in the questionnaire. Moreover, each Answer Set has a set of Answers depending on the answer s contents that have been defined by the questionnaire s creators later for each specific question. Different contents that can be defined for each question will be defined as a Label in the Label class. To see a full description of the ontology go to Appendix A Proposed Application After defining and specifying the generic questionnaire structure and content and then creating the desired restrictions for each specific defined questions, an application has to be created using the generic questionnaire ontology that will be used later by a user to create a questionnaire. Without creating an application using the questionnaire ontology, it 29

38 would be difficult to find out if the ontology works according to the goal of creating a proper questionnaire ontology or not. In order to evaluate the questionnaire ontology, using Java and OWL-API, an application was created to be used to create questionnaires and can also be considered as an evaluation method for the ontology. No reasoning has been developed directly in the Java application as the ontology reasoner was used as the main reasoning tool in this work. The application can be used to create questionnaires to make sure the restriction that has been created in the ontology is working according to the category that is specified. Moreover, the application is ready to be used for the creation of the desired questionnaire to be used for data collection. 3.2 Creating Questionnaire Ontology by Using Domain Ontology for Error Checking Purposes One of the important aspects of this research is to use the ontology reasoner for error checking purposes. By error checking we mean that the questionnaire s creator wants to create a question and for that specific question wants to apply the restriction from a domain ontology class which has related content to that specific question and use the restriction against the question answers that will be created by questionnaire s creator. Therefore, restrictions will be checked against the answers that are created by questionnaire s creators to find out if they are in the specified restrictions or not. Furthermore, it will help them to make sure their questionnaire is accurate according to specific contents that it was created for. In order to use the domain ontology for error checking purposes, in this research we propose a prototype that will be defined in the next section. 30

39 3.2.1 Proposed Prototype In this research, we use a Semantic Web Rule Language (SWRL) engine to infer new knowledge regarding individuals by chain of properties [1]. A description Language (DL) reasoner is used in this research and specifically the Pellet reasoner has been used which is integrated in Protégé. In order to continue with this aspect, first we need to create the generic questionnaire ontology and make sure that all the restrictions are created properly. Then, the generic questionnaire ontology will be used by the questionnaire s creators to create a questionnaire on a specific domain. The following process has been taken to create the proposed prototype that can be used as an error checking mechanism when creating a questionnaire. 1. The application will ask the user to insert the first question s text. 2. After creating the content of the first question, the user will be asked if he wants to use the domain ontology for error checking or not. In this prototype we are choosing to use the domain ontology. 3. If the question wants to be checked for error checking, the user will choose which domain ontology should be used for this specific question. The domain ontology that will be merged with the generic questionnaire ontology depends on the question text that was inserted in the first part of creating the question. Therefore, the content of the question will specify which domain ontology needs to be merged with the questionnaire ontology. 4. After specifying the domain ontology that will be used for this specific question by user, the domain ontology will be merged with the generic questionnaire ontology. The user has to choose the word from the question that was used to pick the domain ontology. 31

40 5. Next, the user has to choose the class from the domain ontology that has to be used for this specific question. In order to merge the domain ontology with the questionnaire ontology, more classes have been added to the questionnaire ontology to capture the relevant information. Domain class and Domain Ontology class are two classes that have been added. The description of each of these classes with the properties that they included can be found in this section. Figure 3.5 on page 32 shows where Domain Ontology class and Domain Class have been added to the questionnaire ontology. Figure 3.5: Added Ontology Structure to Keep Domain Ontology Contents 32

41 Domain Class: In order to keep this information in the ontology and use it for error checking later, the class DomainClass has to be added to the ontology to keep the name of the class from the domain ontology that is selected by the questionnaire s creator. Data Properties: None Object Properties: isdomainclassof Domain Ontology: The class DomainOntology has been added to capture the information about the domain ontology that was chosen by creator. As the domain ontology that will be chosen by creator depends on the content of the question, it has to be imported into the questionnaire ontology. The DomainOntology will store the ontology name and ontology IRI by using has Domain Name and has Domain IRI data properties. The domain ontology information is stored as the system tries to prevent importing the same ontology twice into the questionnaire ontology in case the questionnaire s creator selects the same domain ontology for multiple questions. The system will check to see if it has the domain ontology on the list of DomainOntology class instances and use that as a reference to what it needs. The Question class is linked to DomainOntology class with the hasdomainontology object property. In case the user chooses to use a domain ontology for specific individual questions, both classes will be created and will be linked together in order to find out which domain ontology has been merged with which question. Multiple questions can be linked to the same domain ontology. Data Properties: has Domain IRI, has Domain Name Object Properties: hasdomainclass, isdomainontologyof Figure 3.5 on page 32 shows the proposed structure of the questionnaire ontology that has been changed in order to merge the domain ontology into the questionnaire ontology. This 33

42 structure has been added to the questionnaire ontology and nothing has been deleted from the structure of the generic questionnaire ontology. By merging the questionnaire ontology, we will use the terms that are created in the question and specified by the questionnaire s creators as important terms that should be looked up in the domain ontology to find out exactly what restrictions have been defined for those terms. The application that has been implemented in this research works as an interface between the questionnaire s creators and the questionnaire ontology to make the work easier and more efficient. In the next section, the proposed application that has been developed and used in this research will be defined Proposed Application This prototype application will ask the questionnaire s creators a series of questions to populate the questionnaire ontology by using the domain ontology. Some of the questions asked by the application are explained in Section The following process has been taken in the application layer in order to create an error checking system that will be used while creating a questionnaire. 1. After the user has defined the domain ontology and domain ontology class, the application will import the domain ontology class from the domain ontology to the questionnaire ontology. 2. In this step, the application will go through the domain ontology class that was specified by the user to look for and find the restrictions and properties that were created for this specific class. 3. Based on the properties that the specific domain ontology class has, a SWRL rule will be created. Class properties in this case means data properties from the domain 34

43 ontology that have the specified class as their Domains. These data properties will be used as part of the SWRL rules to create a rules for each specific question. Therefore, depending on the question that is chosen by the questionnaire s creators and the domain ontology and specific domain ontology that is chosen from that domain ontology the SWRL rules will be created for that specific class. The SWRL rule is designed to be created in the application as the questionnaire s creators will choose first if the domain ontology needs to be merged with that specific class in the application and then depending on the data, SWRL rules will be created. SWRL rules will be created only if the questionnaire s creators choose to import the domain ontology into the questionnaire ontology. Therefore, SWRL rules will be the compound of questionnaire ontology individuals and selected domain ontology classes by the questionnaire s creators. If a domain ontology s chosen class is not defined on the domain s data properties, there is no need for SWRL rules to be created. 4. In order to use SWRL rules in the ontology, the Pellet reasoner has been used which is the most compatible ontology reasoner that works well with SWRL rules. 5. In this step, the application will ask the user to choose which types of questions they want to create. They will have the the choice of selecting questions that are defined in the questionnaire ontology based on their needs. 6. The application will ask the user how many answers they want to create for this specific question. Therefore, depending on the types of questions they had selected, they will need to create the answers. 7. Any SWRL rules that were created previously and contain properties from the domain class ontology will be checked against the answers that are going to be created by the questionnaire s creators to make sure that those answers are following the restrictions 35

44 that are defined in the domain ontology. 8. The SWRL rule is applied to this specific question by running the ontology reasoner which will find out if the answer is proper according to the domain property restrictions or not. The SWRL rules will be checked against each answer label separately by the time they are inserted in the questionnaire. 9. In order to capture which answer labels have the correct data ranges and are proper according to the properties from the domain ontology that were selected or not a class having corrected data range has been created to be used in SWRL rules as the head of the rule. The having corrected data range class will keep the Answer label individuals that have the corrected data range and would be placed as inferred individuals of having corrected data range class by running the Pellet reasoner. The having corrected data range class has been created as a class in the questionnaire ontology. 10. The information from the ontology will be used in the application to check the answer labels and make sure that the questionnaire s creators created an accurate question/answers according to the domain ontology or not. If the answers are not proper according to specified restrictions, the questionnaire s creators will be informed and asked to insert the proper data until all the answer labels are proper. In this case the ontology and the reasoner plays an important role in error checking. Figure 3.6 on page 40 shows the different steps that have been taken to create the proposed application. 36

45 3.3 Modifying the Questionnaire when the Domain Ontology Content Has Been Modified In terms of creating questionnaires by merging domain ontology, the domain ontology s contents play an important role in the accuracy of the questionnaire s answer sets. Therefore, changing the content of the domain ontology has to affect the questionnaire s answer sets that are connected to the domain ontology s content. In this part of the research, we are looking at the questionnaires that has been created using the generic questionnaire ontology and merged domain ontology to be used for error checking purposes. Over time, the content of the domain ontology may be modified and changed. More contents and properties could be added or current contents could be modified. Therefore, as the domain ontology has been used before during the creating of one or more questions in a questionnaire, it is essential that the newer edition of the domain ontology is applied to the questions that previously used the domain ontology. This must be done to make sure that the questionnaire s answer sets are proper according to the newest revision of the domain ontology. In this research, this functionality has been implemented to create more flexibility for the questionnaire s creators Proposed Prototype and Application The following process has been taken to create a prototype in order to modify a questionnaire when a domain ontology has been modified. 1. In this work, first we need to load the questionnaire ontology that has been previously created. The questionnaire ontology can be loaded locally or from an online resource. 2. Then in this application, the list of questions that are included in this questionnaire 37

46 ontology will be represented. In addition to the list of questions, it will be shown if they have been merged with a domain ontology for each question or not. 3. If the domain ontology has been used for a question the name of domain ontology, the source of where the domain ontology was loaded from, and the domain ontology class that had been chosen previously in the domain ontology will be listed here. 4. In the case that the domain ontology has been modified, parts of the domain ontology that have been used in the current questionnaire need to be modified and newer versions of them have to be used by the questionnaire. Therefore, in the application, after presenting questions with the domain ontology and domain ontology classes that they have been merged with for error-checking purpose, the questionnaire s creators will be able to choose which domain ontology class they would like to modify. 5. When the questionnaire s creators provide the domain ontology class name to the application, the application will search to find out which questions used this domain ontology class. In this part of the work, the structure that had been added to the questionnaire ontology as explained in the last section will be used. 6. By defining the questions that use that domain ontology class, SWRL rules for those selected questions will be deleted. SWRL rules have to be deleted as they contain information about the domain ontology class that has been changed. 7. The questionnaire ontology also contains object properties and data properties related to specified domain ontology classes. Therefore, after deleting the appropriate SWRL rules related to specified domain ontology classes, those related data properties and object properties for the domain ontology classes have to be deleted. 8. Later the domain ontology class itself will be deleted from the questionnaire ontology in order to import the newest version. 38

47 9. The application will ask the questionnaire s creators where the new domain ontology class will be imported from. By specifying the location of the new domain ontology, the application will take the newer version of the domain ontology class and related data and object properties from the domain ontology and add it to the questionnaire ontology. In these circumstances, the questionnaire ontology will include the most up to date content about the specified domain ontology class. 10. Therefore, new SWRL rules will be created for questions that use the specified domain ontology class for error checking purposes. 11. By creating new SWRL rules and running the Pellet reasoner in the application, the application will find out questions that use the specified domain ontology class for error checking if the answers that were created previously are proper according to the newer version of the domain ontology class contents or not. In questions that use a specified domain ontology class, the application will check each answer separately and if the answers are not proper according to the SWRL rule that has been created, the questionnaire s creators will be asked to insert an answer that is proper according to the defined SWRL rule restriction. 12. At the end, the newer revision of the questionnaire ontology will be created and stored and can be used by the questionnaire s creators. Figure 3.7 on page 41 shows the different steps that have been taken to create the proposed prototype and application. 39

48 Figure 3.6: Proposed Application Steps 40

49 Figure 3.7: Proposed Prototype and Application Steps 41

50 Chapter 4 Case Studies In this Chapter, we provide a description of case studies used in this Thesis. We start by providing the context of each case study and a brief discussion of the purpose of the case study. We then provide the details for each case study. 4.1 Background and Purposes This Thesis set out to do research on developing a generic questionnaire framework using an ontology to be used to create different questionnaires in different domains and merging domain ontologies to be used as a proper tool for error checking. The main goal of this research is to create a generic framework to facilitate creating questionnaires in different domains and to make sure that it has enough accuracy by using the power of a reasoner to check the restrictions that were defined in the ontologies. In this case, it is important to show how this research creates less effort for the questionnaire s creators in terms of minimal hard coding and creating more accurate sets of questions by using ontology reasoners. Furthermore, another important goal is the creation of a questionnaire ontology structure that can be expanded later with less effort compared to the time in a system where no ontology was used in creating the questionnaires. It has to be considered that expanding the structure of questionnaires and modifying what has been created inside the ontology do not need any specific knowledge of computer science and a person with knowledge of the ontology and 42

51 the structure of their questionnaire would be able to create, expand or modify the ontology driving the questionnaire. The case studies, therefore, are used to demonstrate the generic structure of the questionnaire ontology that has been created and how the reasoner will help to check the restrictions that are defined in the ontology. In all case studies, the main goal is to use the generic questionnaire ontology to create a domain specific questionnaire. However, we expand our work and we will use the power of ontologies and reasoners to merge the generic questionnaire ontology with a domain ontology to be used as a error checking system for the questionnaire creator while designing a domain specific questionnaire. The questionnaire that is created in our case studies using the generic questionnaire ontology is a medical questionnaire that would be used by nurses to get information about a patient s temperature. Questions that will be created during these case studies are not realistic questions and they are only created for the purpose of this research. However, a small demonstration medical domain ontology as a domain ontology will be used for merging with the generic questionnaire in this research. The medical domain ontology has been created for the purpose of this research and cannot be considered and applied as a concise medical domain ontology. Three different case studies have been developed in this research to address three different goals: The first case study will be focused on creating a medical questionnaire using the generic questionnaire ontology without using a domain ontology to show the power of the generic questionnaire ontology and reasoner to create different questionnaires in different domains. The second case study will emphasize the merging of a medical domain ontology with the generic questionnaire ontology to be used for error checking purposes. The third case study is focusing on when a questionnaire has been created using the generic questionnaire ontology and merged domain ontologies (Example: case study 2). Over time, the questions have to be checked and modified in case the content of the domain ontologies have been 43

52 changed Case Study1: Creating a Questionnaire Without Using a Domain Ontology In our first case study, we decided to go through creating a questionnaire using the generic questionnaire ontology without using a domain ontology. The goal is to see how the questionnaire would be created and how the reasoner is used to check the restrictions that are defined in the ontology. In this case study, the goal is to demonstrate how generic questionnaire ontology has been used to facilitate computer based questionnaires. The generic questionnaire structure has been defined in the ontology. The current system using generic questionnaire ontology compare to former computer based questionnaire has minimal hard coding as questionnaire has been defined in the ontology first. In addition, application will use ontology reasoner instead of developing reasoner in the application. Therefore, application has been developed to facilitate creating the questionnaire for questionnaire creators. In this case we will design a medical questionnaire using the generic questionnaire ontology. As we mentioned before, the questionnaire s creator can choose from seven different types of questions that were defined before. We defined all structures and the restrictions that need to be defined for these questionnaire types in the ontology and now we run Java code that was developed to use the framework to create a questionnaire using the ontology. Therefore, in this case we do not need to work on the ontology itself directly and we will open the ontology in the last part to make sure that the questionnaire was created successfully and accurately. In this scenario Figure 4.1 on page 45, the questionnaire will be created using the generic questionnaire ontology. We will go through creating a questionnaire in the following sections. 44

53 Figure 4.1: First Scenario Questionnaire Steps in Creating a Questionnaire In this section, by creating questionnaire using ontology, we will go through different steps to realize how the generic questionnaire ontology that has been created before will be used to create a medical questionnaire. Besides, how the questionnaire structure that has been defined in the ontology will be used in creating a medical questionnaire. In the first step, we need to define how many questions will be created in this questionnaire. In this scenario, we will create only one question for this questionnaire. Figure 4.3 on page 46 is the question that will be asked by the application. Figure 4.2: Number of Question will be Asked by Application The questionnaire will be created with Question1 and a question individual with the assigned 45

54 number will be created in the ontology. Further, we need to enter the question text that in this case would be: What is the patient s temperature?. Figure 4.3: Question Text will be Asked by Application The question text is defined as a data property value for this question. In the next step the questionnaire s creator will be asked if they want to use a domain ontology for this question. As this case study is focused on creating a medical questionnaire using the generic questionnaire ontology, we do not merge a domain ontology in this question. By refusing to use the domain ontology for this question, the questionnaire s creator will be asked Which type of question do you want to choose? In this case we will choose Multiple Choice Single Selection since one answer is to be chosen by the respondents and multiple choices are offered to the respondents to choose from. The restrictions about different types of questionnaires is defined in the ontology and it is the questionnaire s creator responsibility to choose which type of questions they wanted to create to be categorized under that category. For example, if the survey creator intends to create a Multiple Choice Multiple Selection question but then in the application will identify that the user has only one selection then it will be created under the Multiple Choice Single Selection category. Furthermore, depending on the presentation of different types of question which will be developed by application developer, questions will be represented differently. After identifying which type of questions will be created, different types of questions will be asked to the survey creator that will be varied depending on the type of questions. In the case of a Multiple Choice Single Selection question, the question creator needs to specify how many answers they want to create. In this case we 46

55 will choose to create four answers. This number would be defined for each questionnaire as a data properties value. Then depending on the number of answers that were specified by the creator, Answer Label individuals will be created. Now for each Answer Label we will ask the creator to specify the contents of that Answer Label and other restrictions about Answer Label that are defined in the ontology. The question creators need to create their questionnaires in the application and at the final stage will refer to the ontology to find out that their questionnaires are created properly. In this case study, we have demonstrated how the generic questionnaire ontology facilitates creating different questionnaires and making sure about the structure of questionnaires by using the ontology reasoner. In addition, the amount of work that needs to be done for editing the structure of the questionnaire or adding the new type of questions is mainly be done in ontology and the amount of written code is minimized Application Perspective According to the different steps that have been taken to create a questionnaire in section , different questions have been asked by the application that has been created for these purposes. Figure 4.4 on page 48 shows the whole process that has been taken on the application side to create the questionnaire for this scenario Ontology Changes According to different steps that have been taken to create a questionnaire in section , different changes have been done in the ontology to create a questionnaire using the generic questionnaire ontology and reasoner. Figure 4.5 on page 49 shows the question that has been created in the ontology according to the question number. Also it shows the question text that has been created as a data property value for this question. Note: During creation of 47

56 Figure 4.4: Application Steps in Creating a Questionnaire the ontology, a spelling error on label happened. It is misspelled in the ontology. By choosing the question type that we would like this question based on it, which would be multiple choice single selection in this scenario, the MCSS1 individual will be created in the ontology. Furthermore, by defining the number of answers that we would like in this question have, which would be four in this case, different data properties and object properties will be added to this individual in the ontology. Figure 4.6 on page 50 shows different changes that will occur in the ontology for this section. Furthermore, an answer individual will be created in the ontology. This individual has four answer labels as we defined four answers in the former steps. Figure 4.7 on page 51 shows answer individual and answer labels that has been created in this step. You will notice that all the individuals that have been created for this question have carried the same question 48

57 Figure 4.5: Creation of Questions and Their Components in Ontology number in their name so that they could be identified more easily in the ontology. Moreover, in the last step by defining the answer label by the questionnaire s creator, this data will be added as a data property value to the ontology. Figure 4.8 on page 52 shows what has been defined in the ontology in terms of answers that the questionnaire s creator has created. Therefore, in this case study we prove that generic questionnaire ontology can be used to create questionnaire and as it has its own reasoner, we will have minimal hard coding in the application. As a result, using generic questionnaire ontology facilitates computer based questionnaire Case Study 2: Creating a Questionnaire Using a Domain Ontology In this case study, the goal is to demonstrate how the generic questionnaire ontology can be used in different domains. As we mentioned earlier, generic questionnaire ontology only contains the structure of questionnaires and it does not include the content of any domain. Therefore, it could be applied to create questionnaires for different domains. In addition, this 49

58 Figure 4.6: Creation of Question Type Individual in the Ontology case study will demonstrate that domain ontology can be merged to questionnaire ontology by importing domain ontology to questionnaire ontology and creating SWRL rules to be used as an error checking mechanism in questionnaire creation. In the second case study, we decided to create a questionnaire using the generic questionnaire ontology by merging with a domain ontology for error checking purposes. In this case we will design a medical questionnaire using the generic questionnaire ontology. The application that was developed before will be used in this case study as well to create the questionnaire. The application has been designed to create more possibilities for the questionnaire s creators to create and design a specific question easily. We will go through creating a questionnaire by merging with a domain ontology in the following section Steps in Creating a Questionnaire Using a Domain Ontology These are the different steps that have to be taken by questionnaire s creators to create a questionnaire ontology using a domain ontology. In order to create the questionnaire an application has to be used. In the first step it will be asked how many questions would like 50

59 Figure 4.7: Creation of Answer Individual in the Ontology to be created. For each question that will be created the number that will be assigned to all individuals of that question is the same as the question number as we need to capture and trace all the information of each specific question to do the realization and reasoning easily. In this scenario, we will create only one question for this questionnaire. Therefore, the question that will be created in this questionnaire is Question1 and a question individual with the assigned number will be created in the ontology. In the next step, for the first question, the application will ask for the question text to be inserted. In this case the question text that will be inserted is What is the patient s temperature?. Afterwards, the questionnaire creator has the choice to select the a domain ontology for this question or not. In this case as we want to use the domain ontology for error checking, we need to select the domain ontology to import into the questionnaire ontology. In the next step will be asked where the domain ontology will be imported from. Therefore, the questionnaire s creator could choose to load the domain ontology from a local file on the computer or from an online location. In this scenario the domain ontology will be imported from a local location. However, both online and local locations have been tested for this application. By importing the domain ontology into the questionnaire ontology, a domain ontology individual will be 51

60 Figure 4.8: Creation of Answer Label Individual in the Ontology created in the ontology. As we want to avoid multiple imports of the same domain ontology into questionnaire ontology, the domain ontology individual will store the domain name and domain IRI of the domain ontology as data property values in the questionnaire ontology. After importing the domain ontology into the generic questionnaire ontology, the user will be asked to choose which word from the question they want to focus on when using information from the domain ontology for this specific question. Refer to Chapter 3 s introduction as we mentioned that the question text is the semantic part of a question that will be focused on. Then the user will insert the word from the question text and in this case, the selected word from the question text will be temperature. This information will be stored in the ontology as a data property of the question individual. The user then has to choose which class from the domain ontology they want to use for this specific question. By selecting the domain class from the domain ontology, that in this case study would be class Temperature, this information will be stored as an object property range for the domain ontology individual. The application will go through this class in the domain ontology and find out if there are 52

61 properties defined in this ontology that have the class Temperature as its domain. In this case study the domain ontology has a data property that has class Temperature as its domain and there is a range defined for this data property. Figure 4.9 on page 53 shows the data property that has been defined for class Temperature in the domain ontology. Figure 4.9: Domain Ontology Data Properties Therefore, the application will pick this data property and based on the question that is created now and this data property a SWRL rule will be created in the questionnaire ontology. The following is the SWRL rule that has been created in this step. Answer(AnswerIndv1), xsd:float[>= 34.7f, <= 43.1f](?x), haslable(answerindv1,?n), has Lable Text(?n,?x) having corrected data range(?n) Figure 4.10 on page 54 shows the SWRL rule that has been created in the questionnaire ontology based on the question that has been defined before and the domain ontology. In the next step, the application will ask the questionnaire creator which type of question they want to choose, and in this case we will choose the Multiple Choice Single Selection category. Then in the case of Multiple Choice Single Selection we will be asked how many 53

62 Figure 4.10: SWRL Rules Created in the Ontology questions do you want to create? and we will choose four as want to have four answer choices. Therefore, we need to insert the content for four answer choices in this step; for each answer choice an answer label individual will be created in the ontology. For the first answer label after inserting an answer for this question, first of all, the application will look at the type of property that it has in the SWRL rules and will store all the answers of the same type. Therefore, in this case, all answers will be stored in type float. After inserting the first answer label, the reasoner will be run in order to find out if the answer is in the correct range according to the SWRL rule that was created. If the answer is in the correct data range then will ask the user to insert the correct answer label and will continue this process till all the answer labels are considered to be in the correct data range. In this scenario, if we try to insert 45 as an answer choice, the reasoner will check the data against the SWRL rule that has been created and print a message for the user in the application to insert the correct answer choice in the application. Therefore the answer label that has a correct data range will be stored in the ontology and others will be ignored and not stored in the ontology. All the connections in the ontology will be created according to the information explained in the Methodology section to create correct connections between classes in the ontology. The ontology that is created at the end of this case study includes the generic questionnaire ontology and domain ontologies that are inserted for this specific questionnaire and all the information that is created by the questionnaire creator for this specific questionnaire. It does not make changes to the generic questionnaire ontology and 54

63 would be stored as an OWL file as a specific questionnaire ontology. As a result, merging the domain ontology into the questionnaire ontology has been used for error checking purposes Application Perspective According to the different steps that have been taken to create a questionnaire in section , different questions have been asked by the application that has been created for these purposes. Figure 4.11 on page 55 shows the whole process that has been taken on the application side to create the questionnaire by using the domain ontology for error checking in this scenario. Figure 4.11: Application Steps in Creating a Questionnaire Using a Domain Ontology 55

64 Ontology Changes According to different steps that have been taken to create a questionnaire in Section , different changes have been done in the ontology to create a questionnaire using the generic questionnaire ontology and merging a domain ontology based on a question s text content. Figure 4.12 on page 56 shows the domain ontology that has been imported into the questionnaire ontology based on the steps that have been taken before in section to import the domain ontology. Figure 4.12: Domain Ontology Imported into the Questionnaire Ontology The first step when we define a questionnaire is creating a question, therefore a question 56

65 individual has been created in the ontology according to question number. Figure 4.13 on page 57 shows the question individual and question text that has been created as a data property value for this question. It also shows that the word temperature from the question text has been chosen. In addition, this question is merged with a domain ontology. Therefore, a domain ontology individual has been created. Also, the domain class Temperature from the ontology has been chosen. Furthermore Figure 4.13 shows that this question is of the Multiple Choice Single Selection question type. Figure 4.13: Creation of Question Individual into Ontology in case of Using Domain Ontology By choosing a domain ontology to be merged and used for creating a questionnaire, a domain ontology individual will be created in the questionnaire ontology. Figure 4.14 on page 58 shows the domain ontology individual that has been created. Also, the information about the domain ontology name and IRI will be stored as data properties for this individual. The reason to keep this information is to avoid multiple imports of the same domain ontology into a questionnaire ontology. We also store the domain ontology class that has been used from this domain ontology as a object property value in this section. If different classes of a domain ontology has been chosen for the same questionnaire, all these domain ontology classes will be stored under the same domain ontology individual. 57

66 Figure 4.14: Creation of Domain Ontology Individual by Merging the Domain Ontology into the Questionnaire Ontology Furthermore, by choosing the question type that we would like this question to be based on, which would be multiple choice single selection, the application will ask about the number of answers that we would like this question to have. Figure 4.15 on page 58 shows the number of answers and number of needed selections that we have for this question. In this question we chose to have four answers and as it is multiple choice single selection question type, so by default it has one selected. Figure 4.15: Creation of Question Type Individual in Ontology 58

67 Furthermore, an answer individual will be created in the ontology. This individual has four answer labels as we defined four answers in former steps. Figure 4.16 on page 59 shows the answer individual and answer labels that have been created in this step. Figure 4.16: Creation of Answer Individual in the Ontology Moreover, in the last step by defining answer contents by the questionnaire s creator, this data will be added as a data property value to the ontology. Figure 4.17 on page 60 shows what has been defined in the ontology in terms of answers that the questionnaire s creator creates. As you will notice, the answer content for this answer label uses the SWRL rule that has been created for error checking and it only keeps the correct content. The data that has been saved in this step is saved as a float type as the data property range that has been defined for the domain ontology class was a float type. 59

68 Figure 4.17: Creation of Answer Label Individual in the Ontology Case Study 3: Editing the Questionnaire When the Domain Ontology is Modified In this case study, we will prove how we can edit the created questionnaire when the domain ontology contents have been modified. In the third case study, we decided to work on the questionnaire that had been created in the last case study. The questionnaire that we are going to work on has been created using the generic questionnaire ontology. Furthermore, the questionnaire also has imported domain ontologies to be used for error checking purposes. In this case study the content of the domain ontology that has been used in creating questionnaire has been modified. Therefore, the newer revision of the domain ontology has to be applied to make sure that the question answers that were using the domain ontology before are still concise and accurate. The new application that has been implemented for this case study will be used. In this case the application will be asked to select the questionnaire ontology that had been 60

69 created before. Therefore, we will select the medical questionnaire that had been created in case study 2. By selecting the questionnaire, the application will show the list of questions and domain ontologies and domain ontology classes that each of them imported. In the case that a question does not import any domain ontology, the content of the domain ontologies and domain ontology classes will be null. Then, the application will ask the user the content of which domain ontology class has been changed. We assume that the questionnaire s creator has knowledge of the domain ontology class that has been changed in the domain ontology for the purposes of this case study. In this case the content of domain ontology class Temperature has been changed. By inserting Temperature for the answer of the question that had been asked by the application, the application will look for all the questions that have used this domain ontology class. After finding questions that have used domain ontology class Temperature, it will delete the SWRL rules that have been created based on domain ontology class, Temperature. In this case as the only question that used this domain ontology class is Question 1, then, the following SWRL rule related to this question will be deleted. Answer(AnswerIndv1), xsd:float[>= 34.7f, <= 43.1f](?x), haslable(answerindv1,?n), has Lable Text(?n,?x) having corrected data range(?n) Instead of deleting the whole imported domain ontology into the questionnaire ontology that has been created, in this work only the domain ontology class that needs to be changed will be deleted. Before deleting a specified domain ontology class from the questionnaire ontology, object properties and related data properties to this domain ontology class will be deleted from the questionnaire ontology. In the next step, when we make sure there is no property related to this domain ontology class existing in the questionnaire ontology, the domain ontology class itself will be deleted. Afterwards, the newer version of the domain ontology class will be added to the questionnaire ontology. 61

70 In order to import the newer version of the domain ontology, the application with ask the user to enter the domain ontology path for the newer version. Next, all the data properties and object properties related to this domain ontology class will be imported from the newer version of the domain ontology to the questionnaire ontology. Therefore, now that we have all the domain ontology class contents into the questionnaire ontology, new SWRL rules will be added. The new SWRL rules will include the new restriction that was modified for the Temperature class from the domain ontology. Figure 4.18 on page 62 shows the newer version of the domain ontology that we want to apply to the questionnaire that had been created before. Figure 4.18 shows the data property that has been defined for class Temperature in the newer version of the domain ontology. Figure 4.18: Data Properties of Modified Domain Ontology Application Perspective According to the different steps that have been taken to apply a newer version of domain ontology into questionnaire in section different questions have been asked by the application that has been created for these purposes. Figure 4.19 on page 63 shows the whole 62

71 process that has been taken in the application side to create the questionnaire by using the newer version of the domain ontology for error checking in this scenario. Figure 4.19: Application Steps in Editing a Questionnaire using a New Version of the Domain Ontology Ontology Changes According to the different steps that have been taken to modify the questionnaire ontology based on the changes that have been made in the domain ontology in section 4.1.3, different changes have been made to the questionnaire ontology. Figure 4.20 on page 64 shows the questionnaire ontology classes and domain ontology classes that has been imported into the questionnaire ontology. The domain ontology class Temperature is a newer version of this class that has been imported according to the steps that had been taken in section Figure 4.20 also shows the newer version of the SWRL rules that has been created based on the information from the latest version of the domain ontology. Also, in terms of the answer label individual, as the SWRL rule has been changed for this question, the answer label individual content that has been created before needs to be modified in the application by the user. Therefore, the newer content for answer label 63

72 Figure 4.20: SWRL Rules Created based on the Newest Version of the Domain Ontology individual has been stored instead of the previous content. Figure 4.21 on page 64 shows the changes that are mentioned in this section. Figure 4.21: Modified Answer Label based on Using Newest Revision of the Domain Ontology 64

73 Chapter 5 Conclusions and Future Work 5.1 Conclusions In the motivation of this research we highlighted the lack of having a generic questionnaire ontology that can be used as a main tool to create specific questionnaires in different domains. We highlighted that the main tools that had been used to create questionnaires in different domains mostly lack semantic web specifications and consist of a large amount of hard coding. Therefore, they do not include the properties and specifications that can be find in the systems which use the semantic web contents.thus the reasoning and sharing of the data which are the two main properties of semantic web structures cannot be found in previous systems. Although they were systems created to design a questionnaire using ontologies, they are all defined under specific domains and cannot be considered as a generic questionnaire ontology. Furthermore, we then explored how an ontology can be used to drive the creation of generic questionnaire tools that can be used with different domains. Therefore, in this research in order to create a solution for the lack of having a generic questionnaire tool that can be used in different domains, an ontology driven generic questionnaire ontology has been created. An ontology-driven generic questionnaire has been created to facilitate computer based questionnaire that can be used in different domains. We create generic questionnaire ontology in section 3.1 and demonstrate this work in Section

74 For demonstration and validation, a prototype system was designed and developed using the Java language driven by a questionnaire ontology. The questionnaire ontology that has been created in this research describes the generic fundamental components and structure of a questionnaire and does not contain contents of any specific domain. Furthermore, in this research we answered the question of merging different domain ontologies with a questionnaire ontology that can be used as an error checking mechanism while creating a questionnaire. Therefore, in this research, an error checking mechanism can be used while creating a question by merging a domain ontology that is related to the semantic content of that question text. SWRL rules and an ontology reasoner are the main tools and techniques that were used to address the proposed solution. The approach and proposed prototype for this work has been defined in Section 3.2. Section on Chapter 4 demonstrates how we can merge domain ontologies and use the proposed prototype as an error checking mechanism in questionnaire creation. The next contribution in this research is related to modifying the questionnaire content when the domain ontology content is modified. Therefore, changing the content of domain ontology will change the questionnaire s answer sets that were merged with that domain ontology before. For demonstration and validation, a prototype system was designed and developed using the Java language driven by the questionnaire ontology and a domain ontology. SWRL rules and an ontology reasoner are used in this part of the work for modifying the questionnaire when necessary. The approach and proposed prototype for this work has been defined on Section 3.3. Section on Chapter 4 demonstrates, how we could edit the created questionnaire when the domain ontology content is modified. 66

75 5.2 System Properties and Limitations The proposed ontology-driven generic questionnaire framework and prototype in this research has different system properties and limitations that will be explained in the following sections Scalability In terms of scalability, one domain ontology (medical domain ontology) was created and used in this research for analysis and error checking purposes. However, different domain ontologies can be merged and used with the generic questionnaire ontology for further prototype developments in order to provide further error checking and analysis. The case study in this research was limited to testing with one domain ontology; however this can be expanded for testing with different domain ontologies Robustness In terms of robustness, more testing on the system can be done to ensure the reliability of the system. One of the test cases that can be considered could be through the use of a more complex and large domain ontology. A domain class can have different data properties; therefore creating a SWRL rule based on the restriction of that domain would be more complex. We need to find out if the correct restriction will be picked and used for the error checking mechanism from the domain ontology to be applied on questionnaires answers. As a result, system complexity will affect the system robustness and more testing needs to be done to verify the system reliability. 67

76 5.2.3 Verification In terms of verification, a large scale testing needs to be configured for the case that the system gets larger and complex. The prototype can be expanded to handle more complex ontologies. Moreover, in the case that multiple domain ontologies have similar terms and concepts; the right concept needs to be chosen to apply for error checking analysis in the questionnaire ontology. 5.3 Future Works Framework Regarding the framework that has been created in this research to create a questionnaire, future works can be considered. In this research while merging the domain ontology with the questionnaire ontology, the user will be asked to select the semantic content of the question text to be merged with the domain ontology. This work can be automated later by the system itself to trace the question text to find out the semantic content of the question text. Therefore, instead of asking the user to enter the word from the question text, the data will be selected and entered by the application itself. As the framework that has been created in this stage is a test framework to work on test cases, more work can be done to improve error checking on entry. This work can be considered as the future work in order to use the enhanced framework to produce the desired software. Part of the work that has been done in this research was focusing on error checking to be used in the creation stage to see if answers fit with information within a domain knowledge system. However, error checking can also be used in the input stage to check a user s answers 68

77 to a questionnaire. In this case, the user s answers will be checked against domain knowledge and therefore the user s answers will be accepted or rejected. Therefore, error checking on user s answers could be considered as a future work on questionnaire ontologies. Moreover, one of the advancements possible with this work is through using an analysis ontology as well as a domain ontology with the questionnaire ontology. For this aspect, Henriques[10], has created an analysis ontology that can be used for the possible analysis of questionnaire data as a future work. In this case by using an analysis ontology along with the questionnaire ontology, valid analysis of questionnaire data will be possible over multiple related instances of a questionnaire. In addition, using the analysis ontology along with the domain ontology, an application could create recommended analysis techniques over the specific domain. Figure 5.1 on page 70 shows the structure of potential development of using the questionnaire ontology along side domain ontologies and analysis ontologies. Part of the structure that is colored is the section that has been done in this research work and the rest of that as we mentioned in this section, will be considered as future work Questionnaire Ontology Regarding the generic questionnaire ontology that has been created in this research, a group of question types identified based on researches that have been done through questionnaires used in variety of domains. However, the questionnaire ontology can be expanded and new questions can be added to the ontology later as a future work. 69

78 Figure 5.1: Future Work Structure Merging Questionnaire Ontology with Domain Ontology Regarding merging the domain ontology with the questionnaire ontology, in this work the domain ontology that has been used for this research has been created as a test domain ontology and has been used for this case studies only. However, more research can be done on the merging questionnaire ontology with the real domain ontologies that are been used in different domains. In this research we were also looking on versioning and updating through changes and modifications in the domain ontology. This work has been done on the test domain ontology and can be applied to dynamic domain ontologies to work more on versioning and updating 70

79 techniques Questionnaire Structure This work concentrates on the semantic content of the answers and questions in the questionnaire format. Ordering and flow of questions is obviously a part of a questionnaire s structure but are not considered in this work. The rationale was that the semantic content of the ordering and flow of the questions in a questionnaire could not effectively be approached until the semantic content of the questions and answers were addressed. The work on the generic questionnaire ontology will provide the proper foundation for work on the semantics of question ordering and flow. 71

80 Chapter 6 Appendix A 6.1 Overview of the ontologies Overview of the Generic Questionnaire Ontology In this section a complete structure of the generic questionnaire ontology developed in this thesis to be used for creating questionnaire ontology in different domains is presented. As it is common for most ontologies the Thing class is the super class of all classes in ontology. Figure 6.1 describes the hierarchy of this class. Figure 6.1: Overview of the Thing Class Hierarchy 72

81 Figure 6.2: Overview of the AnswerSet Class Hierarchy Figure 6.3: Overview of the Answer Class Hierarchy Figure 6.4: Overview of the Label Class Hierarchy 73

82 Figure 6.5: Overview of the Question Class Description Figure 6.6: Overview of the Question Class Description 74