Introduction to ontologies

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1 Introduction to ontologies 1

2 Definition Webster : That department of the science of metaphysics which investigates and explains the nature and essential properties and relations of all beings, as such, or the principles and causes of being. Définition du Free On-line Dictionary of Computing Free On-line Dictionary of Computing :formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them. 2

3 Definition (2) Still from the Free On-line Dictionary of Computing : The hierarchical structuring of knowledge about things by subcategorising them according to their essential (or at least relevant and/or cognitive) qualities. 3

4 Definition (3) Still from the Free On-line Dictionary of Computing : For AI systems, what exists is that which can be represented. When the knowledge about a domain is represented in a declarative language, the set of objects that can be represented is called the universe of discourse. We can describe the ontology of a program by defining a set of representational terms. Definitions associate the names of entities in the universe of discourse (e.g. Classes, relations, functions or other objects) with human-readable text describing what the names mean, and formal axioms that constrain the interpretation and well-formed use of these terms. Formally, an ontology is the statement of a logical theory. 4

5 General presentation In information science (and AI), the objective is to represent in a generic and consensual way the knowledge of a domain in order to use and share it among agents (human, machines). by using, we mean the fact to construct new applications from existing components. by sharing, we mean that several applications use the same resource. 5

6 Gruber s definition An ontology is a formal, explicit specification of a shared conceptualization [Gruber93]. Formal, in order to make the ontology interpretable by machines. Explicit specification : to express that concepts and constraints are defined concretly and explicitly. Shared : an ontology captures consensual knowledge Conceptualization : because it refer to an abstract model of a domain. This model proposes relevant concepts of this domain. A translation approach to portable ontology specifications Knowledge acquisition, 5 pp

7 Fensel s definition ontology, through formal, real-world semantics and consensual terminologies interweave human and computer understanding of symbol. Ontologies : Silver bullet for Knowledge Management and Eletronic Commerce. Springer- Verlag

8 Types of ontologies For Guarino, there are 3 types of ontologies : Top-level ontologies :they describe general and independent concepts for a peculiar application domain. Examples : WordNet et Cyc Domain and tasks ontologies : they describe the concepts for respectively a domain and a generic task. Application ontologies : they describe the concepts used in an application, hence they dependent on a domain and a task. 8

9 More types of ontologies There are other interpretations of ontologies. Studer and Fensel distinguish : static ontologies decomposed in generic ontologies (close to Guarino s Top-level ontologies), Domain ontologies, Application ontologies, Representational ontologies which describe the representation of entities without defining what is represented. and ontologies that oriented toward solving a given problem : method and task ontologies. 9

10 Expressivity of ontologies McGuinness(*) distinguishes a set of formalisms and expressivities levels in an ontology spectrum : Controlled vocabulary : a list of terms Thesaurus : a controlled vocabularyun ordered so that it enables the use of equivalence or ordered relations. Informal taxonomy : a classification in the form of a hierarchy (generalization/specializa but there isn t a a strict inheritance relation (the instance of a class is not necessarily an instance of its super class). D.L. McGuinness : Ontologies come of Age. In Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press,

11 Expressivity of ontologies (2) Frame : a frame regroups a set of properties which are inherited by instances and sub classes. Value restrictions : properties are typed. Logical constraints : constraints on the values of the properties are added. Contraints of FOL : very expressive with the possibility to define constraints between terms and relations (disjunction, inverse, etc.). 11

12 Ontology spectrum 12

13 Properties of an ontology Clarity : support for necessary and sufficient and necessary conditions. Consistency : to enable consistent inferences. Extensible Minimal ontological commitments Other properties : disjointness of concepts, support of multiple hierarchies, modularity, etc.. 13

14 Qualities from an ontology all named classes can be instantiated Capture intuitions of domain experts Ensure a minimum of redundancies, thus limit non-necessary use of synonyms Descriptions of ontology elements are well detailed 14

15 Application domains For Fensel, there are 3 main application domains Knowledge Management (KM) Enterprise Application Integration (EAI) E-commerce, a form of EAI which can be decomposed in 2 sub domains B2B (Business-to-Business) and B2C (Businessto-Consumer). 15

16 KM From Dictionary.com : the technologies involved in creating, disseminating, and utilizing knowledge data. Example of use : information research on the Web (filtering out non relevant results) 16

17 EAI From Dictionary.com : The use of middleware to integrate the application programs, databases, and legacy systems involved in an organisation s critical business processes. Example : Storage, consultation and maintenance of data associated to CRM (Customer Relationship Management) in different departments of a company (sales, marketing, etc..) 17

18 E-commerce From Dictionary.com : Commerce that is transacted electronically, as over the Internet. Ontologies can be integrated to ease, improve cooperations, data, information and knwoledge exchangee between companies. 18

19 Components of an ontology Concepts or classes : organized in hierarchies relations between concepts define interaction between concepts functions which are special forms of relations individuals or instances of concepts. 19

20 Languages Interest in ontologies is starting in the domain of KR in the 80s : KL-ONE, CLAS- SIC In the 90s : Ontolingua uses KIF (Knowledge Interchange Format based on FOL) Also in the 90s, for the Web : SHOE, to annotate Web pages (code integration in HTML documents), Ontobroker to annotate HTML documents with references to external ontologies, RDFS(1999), DAML+OIL (2001), OWL (2004) itemize 20

21 (Re)usability of ontologies The more resuable, the less usable. [h] 21

22 Methodologies to design ontologies Several solutions : TOVE (Toronto Virtual Enterprise) SENSUS as for a domain-specific ontology Methontology 22

23 McGuinnes/Noy approach Les étapes proposées sont : 1. Déterminer le domaine et le type d utilisation de l ontologie 2. Considérer la réutilisation d ontologies existantes 3. Enumérer les termes importants de l ontologie 4. Définir les classes et la hiérarchie des classes. 5. Définir les propriétés des classes. 6. Définir les valeurs/types/cardinalités des propriétés 7. Créer des instances 23

24 Methontology 1. Spécification 2. Knowledge acquisition 3. Conceptualisation 4. Intégration 5. Implémentation 6. Evaluation 7. Documentation 24

25 Methontology - specification La spécification d une ontologie, quelque soit le formalisme (informel, semi-formel ou formel), est un document qui doit comporter : L objectif de l ontologie Le niveau de formalisme de l ontologie (informel, semi-formel ou formel). Enumérer les termes de l ontologie 25

26 Methontology - Knowledge acquisition Les sources de connaissances peuvent être nombreuses : analyse documentaire, réunion (brainstorming), interviews avec des experts, etc.. 26

27 Methontology Conceptualisation Générer un glossaire des termes Séparation des concepts et verbes : concepts classification tree et verbs diagrams. 27

28 Methontology Intégration Pour réutiliser les définitions d autres ontologies : Inspecter les méta-ontologies (les concepts utilisés pour définir une ontologie) en vue de sélectionner l ontologie la plus adéquate. Chercher les ontologies qui possèdent des termes cohérents avec les termes de notre conceptualisation et évaluer les définitions. 28

29 Methontology Implementation and evaluation Codification dans un langage pour les ontologies Vérifier la correction de l ontologie et valider le fait que l ontologie représente bien ce qui a été spécifié. 29

30 Methontology Documentation At eh end of each stage, a documentation needs to be produced : Requirements specification document Knowledge acquisition document Conceptual model document Formalization document Integration document Implementation document Evaluation document 30

31 Example : Wordnet Couvre la langue anglaise à travers une description pour chaque terme et en proposant des relations de synonymies et hyper/hyponymies Le domaine de Wordnet est très large (ensemble d une langue) mais le niveau de détail est bas. Les descriptions sont en langages naturels (pas interprétable par une machine) et les relations entre termes sont basiques. URL : wn/ 31

32 Wordnet (2) The word drug. 2 outputs : a verb and a noun. The noun drug has 1 sense in WordNet. 1. drug (a substance that is used as a medicine or narcotic) The verb drug has 2 senses in WordNet. 1. drug, dose (administer a drug to; They drugged the kidnapped tourist ) 2. drug, do drugs (use recreational drugs) 32

33 Wordnet (3) Terms of the domain of the noun drug : drug (a substance that is used as a medicine or narcotic) CATEGORY TERM- (adj) generic2 CATEGORY TERM- (adj) injectable1 CATEGORY TERM- (adj) uninjectable1 CATEGORY TERM- (adv) orally1, by mouth1 CATEGORY TERM- (noun) magic bullet1 CATEGORY TERM- (noun) drop5 CATEGORY TERM- (noun) active transport1 CATEGORY TERM- (noun) anaphylaxis1 CATEGORY TERM- (verb) potentiate1 CATEGORY TERM- (verb) take a hit1, snort4 33

34 Wordnet (4) Again with the noun : drug (a substance that is used as a medicine or narcotic) PART OF: pharmacopoeia (a collection or stock of drugs) Quelques hyponymes anesthetic, anaesthetic, anesthetic agent, anaesthetic agent (a drug that causes temporary loss of bodily sensations) antagonist (a drug that neutralizes or counteracts the effects of another drug) antisyphilitic (a drug (or other chemical agent) that is effective against syphilis) Quelques hypernymes agent (a substance that exerts some force or effect) causal agent, cause, causal agency (any entity that causes events to happen) entity (that which is perceived or known or inferred to have its own distinct existence (living or nonliving)) 34

35 Example Dublin Core Développé en 1995 pour devenir un standard dans le domaine des métadonnées pour les ressources des libraires digitales du Web. 15 attributs en 3 groupes Contenu : coverage, description, relation, source, subject, title, type propriété intellectuelle : contributor, creator, publisher, rights Instanciation : date, format, identifier, language 35

36 Pas de représentation définie HTML inadéquate pour représenter la structure de DC. Disponible comme RDF Schema

37 Example Cyc Objectif de CYC est de capturer l ensemble de la connaissance du sens-commun (par exemple l espace, le temps) mais avec un niveau de détail très important. Il existe des relations formelles entre termes. Une ontologie complexe, généraliste et d une taille conséquente ( 100K termes, 1M assertions) dévéloppée par Cycorp. Propose des outils pour le raisonnement ( 500 heuristiques). 36

38 Modelling ontologies with frames and FOL Description logics software engineering technologies : UML and OCL (Object Constraint Language) Database technologies : ER and EER (Extended Entity Relation) schema. 37

39 Ontology mediation Interoperation of ontologies require reconciliation : ontology mediation. Two main kinds of ontology mediation : ontology mapping : correspondences between 2 ontologies are stored separately from the ontologies and thus are not part of the ontologies themselves. The (semi)- automated discovery of these correspondences is called ontology alignement. ontology merging : a new ontology is created which is the union of the source ontologies. Challenge : enure that all correspondences and differences between ontologies are reflected in the merged ontology. 38

40 Ontology mismatches Conceptualization mismatches : mismatches of different conceptualizations of the same domain. Explication mismatches : mismatches in the way a conceptualization is specified. 39

41 Conceptualization mismatches Scope mismatche : when 2 classes have some overlap in their extensions (sets of instances) but the extensions are not exactly the same. Model coverage and ganularity, if there is a difference in : the part of the domain that is covered by both ontologies the level of detail with which the model is covered 40

42 Explication mismatches Explication mismatches : mismatches in the way a conceptualization is specified. Modeling style mismatch if either : the paradigm used to specify a certain concept is different (intervals vs points in time) the way a concept is described is different. terminological mismatch : when 2 concepts are equivalent but they are represented using different names (synonyms) or when the same name is used for different concepts (homonyms). 41

43 Encoding mismatch when values in different ontologies are encoded in a different way (kilometer vs miles).

44 Ontology mapping an ontology mapping is a declarative specification of the semantic overlap between 2 ontologies. a mapping language is usually exploited there exists ontologies of mappings : MAFRA (1), RDFT(2) which constitutes the vocabulary for the representation of mappings. (1) : Maedche A, Motik B, Silva N, Volz R. : Mafra a mapping framework for distributed ontologies- EKAW 2002 (2) : Omelayenko : RDFT : A mapping meta-ontology for business integration (KTSM 2002) 42

45 Ontology alignment The process of discovering similarities between two source ontologies. 2 dimensions : schema-based (Anchor-Prompt) and instancebased (GLUE) element-based (Prompt) and structure-based (Semantic matching) 43

46 Ontology merging The creation of one ontology from two or more source ontologies. 2 approaches : input is a collection of ontologies and the output is one ne merged ontology, which captures the orginal ontologies (ex : PROMPT). the original ontologies are not replaced, but rather a view, called bridge ontology, is created which imports the original ontologies and specifies the correspondences using bridge axioms. (ex : OntoMerge) 44

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