Semantic Lifting of Unstructured Data Based on NLP Inference of Annotations 1
|
|
- Osborne Jefferson
- 8 years ago
- Views:
Transcription
1 Semantic Lifting of Unstructured Data Based on NLP Inference of Annotations 1 Ivo Marinchev Abstract: The paper introduces approach to semantic lifting of unstructured data with the help of natural language processing (NLP) technologies. Our approach is based on processing the text fragments with NLP tools to tag some of the natural language words and phrases with semantic annotations. Then these inferred annotations are lifted to ontology level in the form of ontology instances that become preliminary automatic annotations of the target text fragments and can later be optionally confirmed and refined by domain experts. Key words: Semantic Web, Natural Language Processing, Ontologies, Instance Data, Semantic, Lifting, OWL, RDFS, RDF. INTRODUCTION In the field of software engineering structured data is data that can be described with formal data model. A data model explicitly determines the structure of data or structured data. Data models describe structured data for storage in data management systems such as relational databases. In our previous paper [1] we presented approach to semantic lifting of structured data based on transformation rules created from the annotations of the data model with a given set of ontologies. Unfortunately data models do not describe unstructured data, such as word processing documents, messages, pictures, digital audio, digital video, and nontyped data fields (TEXT, CLOB, BLOB, etc) of relational databases. In practice most of the information in the Web today is unstructured and proliferation of digital media and social network services (such as wellknown Facebook, Google+, Twitter, etc) makes the unstructured data overwhelmingly widespread. Hence the need for tools and technologies for processing unstructured data constantly increases. In this paper we introduce an extension of our semantic lifting approach [1] to semistructured and unstructured data. We apply natural language processing (NLP) tools and technologies to processing the text fragments in order to tag some of the natural language words and phrases with semantic annotations. Then these inferred annotations are lifted to ontology level in the form of ontology instances that become preliminary automatic annotations of the target text fragments and can later be optionally confirmed and refined by domain experts. BASE ONTOLOGY AND SPECIALIZED ONTOLOGIES The data managed by computers are never completely stand alone. There are always meta data information (annotations) that describe the raw data even on the most 1 This work was partially supported by Research Project No. D funded by the National Science Fund of Bulgaria.
2 general level that people use to organize, manage and search it. Formally these general annotations about the data can be represented as ontology that we named base ontology. For our examples in the paper we use data from digital library Virtual Encyclopaedia of Bulgarian Iconography [3] and all existing annotation about iconographical objects in the original system are collected in the base ontology that we use throughout our project. In fact the base ontology captures the pre-existing formal data model that any information system has even when it is built unintentionally. For the unstructured part of the information we need a set of specialized ontologies that describe domains that natural language texts refers to. These specialized ontologies are created by domain experts and usually are narrow scoped but with a lot of details in the form of ontology instances that are concrete representations of the ontology concepts of a given domain. In our case we use specialized ontologies for technologies of iconographic objects and for iconographic scenes descriptions. These ontologies have ontology classes for example Primer and ontology instances that represent different instances of the classes for example different types of primer that can be found in real world as oil primer, gum primer, emulsion primer, polymeric primer, combined primer, etc. In our approach ontology instances are the main tool for annotating natural language texts as they correspond to concrete words and phrases that people use. The last detail that we want to emphasize is that the base ontology and the specialized ontologies have to be linked in some way so that the graph of semantic classes (concepts) is transversable and allows the creation of semantic queries (in our case SPARQL [6]) using concepts of different ontologies. Our solution to this problem is to link all specialized ontologies with the base ontology with the help of equivalent classes (in OWL terms owl:equivalentclass). So the base ontology becomes main linking point of the whole semantic network. GENERAL ALGORITM In this section we describe the general algorithm that is used in our approach to semantic lifting of unstructured data. We describe the details of different steps of the algorithms in the following sections together with examples for every step taken from the data [3] used in our project [5]. The input of our algorithm is as follows: Base ontology The list of all data properties from the base ontology, which contain natural language text for annotation. Specialized ontologies related to the annotated texts. Web service for semantic annotation of natural language texts given a set of ontologies in our case we use the ClaRK system [4]. The details of the process of natural language annotation that we employ are given in [7]. This work is also part of the SINUS project [5] but is created as completely separated module and is out of the scope of this paper. The algorithm described here is not dependent of NLP module used and can be used with different ones if they are available. Semantic lifting service. Semantic repository service where the semantic annotations are stored in our case we use OWLIM [2] semantic repository. Output: Semantic annotations inferred from the natural language texts, semantically lifted to the form of unique ontology instances. Basic algorithm:
3 Creating document with natural language texts for semantic annotations. Annotating the text document from previous step with ClaRK system. Lifting annotations to semantic level. Details of these 3 steps accompanied by concrete examples are presented in the following sections: EXTRACTING TEXTS FOR NLP ANNOTATIONS This step uses the base ontology and the list of data properties that contain natural language texts to extract the text fragments in a separated XML document that is sent to the semantic annotation NLP service. The general steps are the following: Load the original non semantic data. Use the semantic lifting service [1] to lift any structured part of the information this step is required for the creation of unique identifiers [1] of the ontology instances. Extract all specified data properties and insert them in the XML document that was negotiated as an interface between lifting service and the NLP annotation service (ClaRK system). Below we represent an example of the iconographic object with unique identifier 31 from [3] with data properties: OWLDataProperty_iconographicalTechnique_has_Description OWLDataProperty_baseMaterial_has_Description. <OWLClass_IconographicalTechnique велатури. Приложена е и техниката пробастър върху позлатата и върху ореола има гравировки. Лаковото покритие е нанесено тънко и равномерно.</owldataproperty_iconographicaltechnique_has_description> <OWLClass_SolidMaterial rdf:about="#owlclass_solidmaterial_a7db65f2b4989e34c402d219b137cbb9"> <OWLDataProperty_baseMaterial_has_Description>Две дъски от иглолистна дървесина мура. Гипсов грунд, нанесен тънко и равномерно.</owldataproperty_basematerial_has_description> </OWLClass_SolidMaterial> ANNOTATING NATURAL LANGUAGE TEXTS The XML document created in the previous step is sent to the NLP semantic annotation service. In our case it is ClaRK [4, 7] system. The inferred annotations are added to the original tags of the XML document with a special tag <annotation />. Every annotation is represented with <property.. /> tag within the annotation tag. The property tags contain class from the specialized ontologies (domain attribute), the name of object property corresponding to selected class (uri attribute) and ontology instance (rvalue attribute). These tags represent formalized semantics of the corresponding natural language text fragment. The property tag looks like the example below: <property domain="owlclass_primer" rvalue="owlindividual_fillerplaster" uri="owlobjectproperty_primer_has_filler" /> In this example the ontology class is OWLClass_Primer the name of its object property is OWLObjectProperty_primer_has_Filler and the ontology instance is OWLIndividual_FillerPlaster. The result of the annotation service applied on the XML document presented in the previous section is shown below:
4 <OWLClass_IconographicalTechnique xmlns="" велатури. Приложена е и техниката " пробастър " върху позлатата и върху ореола има гравировки. Лаковото покритие е нанесено тънко и равномерно. <annotation> <property domain="owlclass_gilding" rvalue="owlindividual_typeofgilding01" uri="owlobjectproperty_gilding_has_type"/> <property domain="owlclass_lacquering" rvalue="owlindividual_thicknessoflacquering04" uri="owlobjectproperty_lacquering_has_thickness"/> <property domain="owlclass_lacquering" rvalue="owlindividual_evennessoflacquering02" uri="owlobjectproperty_lacquering_has_evenness"/> </annotation> </OWLDataProperty_iconographicalTechnique_has_Description> <OWLClass_SolidMaterial xmlns="" rdf:about="#owlclass_solidmaterial_a7db65f2b4989e34c402d219b137cbb9"> <OWLDataProperty_baseMaterial_has_Description>Две дъски от иглолистна дървесина - мура. Гипсов грунд, нанесен тънко и равномерно. <annotation> <property domain="owlclass_primer" rvalue="owlindividual_fillerplaster" uri="owlobjectproperty_primer_has_filler"/> <property domain="owlclass_primer" rvalue="owlindividual_thicknessofprimer01" uri="owlobjectproperty_primer_has_thickness"/> <property domain="owlclass_primer" rvalue="owlindividual_evennessofprimer01" uri="owlobjectproperty_primer_has_evenness"/> </annotation> </OWLDataProperty_baseMaterial_has_Description> </OWLClass_SolidMaterial> LIFTING ANNOTATIONS TO SEMANTIC LEVEL On this step all annotations are lifted to semantic level given the base ontology and the specialized ontologies. The annotated XML document contains object properties and ontology instances from the specialized ontologies. For example, in the previous step we got the following annotations in bold: <OWLClass_IconographicalTechnique xmlns="" велатури. Приложена е и техниката " пробастър " върху позлатата и върху ореола има гравировки. Лаковото покритие е нанесено тънко и равномерно. <annotation> <property domain="owlclass_gilding" rvalue="owlindividual_typeofgilding01" uri="owlobjectproperty_gilding_has_type"/> <property domain="owlclass_lacquering" rvalue="owlindividual_thicknessoflacquering04" uri="owlobjectproperty_lacquering_has_thickness"/> <property domain="owlclass_lacquering" rvalue="owlindividual_evennessoflacquering02" uri="owlobjectproperty_lacquering_has_evenness"/> </annotation> </OWLDataProperty_iconographicalTechnique_has_Description> The lifting algorithm is as follows: Using the specialized ontology we find all paths from the classes represented with their ontology instances in the annotations to the root classes of the specialized ontology. The root classes are all classes of the specialized ontology that are in relation equivalent class to the class of the base ontology The edges of the paths between classes (in fact their instances) are object properties of the specialized
5 ontology. For all intermediate classes (without the root classes), ontology instances are created with the same way as in the algorithm for lifting of structured data [1] and all such instances are assigned unique identifiers [1] in the form of hash values (for example MD5) based on instance property values. The lifting results are stored in the semantic repository and for the root element of the semantic graph is used the corresponding ontology instance that corresponds to the class of the base ontology (not the one of the equivalent class in the specialized ontology). The later is needed to make the explicit link between the instances of both ontologies. As these annotations are created automatically we named them preliminary as in practical applications they usually need to be reviewed and adjusted by domain expert to avoid any errors or discrepancies. That s why in our implementation we denote these annotation in a special way so that one can determine which annotations are preliminary and which are confirmed by human expert. The preliminary annotations are denoted with the suffix _P at the end of the name of the corresponding object properties of the root class for example object property OWLObjectProperty_primer_has_Filler, becomes the preliminary annotation OWLObjectProperty_primer_has_Filler_P. When it is confirmed by domain expert the suffix is removed. Below we present the example used throughout the paper with final semantic annotations applied and in the format used for storage in our semantic repository. <OWLClass_Primer rdf:about="#owlclass_primer_9b2141e798c294d87f0e1f2d59cdd5e0"> <rdf:type rdf:resource=" /> <OWLObjectProperty_primer_has_Filler_P rdf:resource="owlindividual_fillerplaster" /> <OWLObjectProperty_primer_has_Thickness_P rdf:resource="owlindividual_thicknessofprimer01" /> <OWLObjectProperty_primer_has_Evenness_P rdf:resource="owlindividual_evennessofprimer01" /> </OWLClass_Primer> <OWLClass_Lacquering rdf:about="#owlclass_lacquering_40e7680df bbf0ac1f95c65"> <rdf:type rdf:resource=" /> <OWLObjectProperty_lacquering_has_Thickness_P rdf:resource="owlindividual_thicknessoflacquering04" /> <OWLObjectProperty_lacquering_has_Evenness_P rdf:resource="owlindividual_evennessoflacquering02" /> </OWLClass_Lacquering> <OWLClass_Gilding rdf:about="#owlclass_gilding_df1d6429adcbadc255cd101e875e4fb8"> <rdf:type rdf:resource=" /> <OWLObjectProperty_gilding_has_Type_P rdf:resource="owlindividual_typeofgilding01" /> </OWLClass_Gilding> <OWLClass_IconographicalTechnique xmlns="" велатури. Приложена е и техниката " пробастър " върху позлатата и върху ореола има гравировки. Лаковото покритие е нанесено тънко и равномерно.</owldataproperty_iconographicaltechnique_has_description> <ObjectProperty_iTechnique_uses_Gilding rdf:resource="owlclass_gilding_df1d6429adcbadc255cd101e875e4fb8" /> <ObjectProperty_iTechnique_uses_Lacquering rdf:resource="owlclass_lacquering_40e7680df bbf0ac1f95c65" /> <OWLClass_SolidMaterial xmlns="" rdf:about="#owlclass_solidmaterial_a7db65f2b4989e34c402d219b137cbb9"><owldataproperty_iconographical Object_has_URI>31</OWLDataProperty_iconographicalObject_has_URI> <OWLDataProperty_baseMaterial_has_Description>Две дъски от иглолистна дървесина - мура. Гипсов грунд, нанесен тънко и равномерно.</owldataproperty_basematerial_has_description> <ObjectProperty_base_has_Component rdf:resource="owlclass_primer_9b2141e798c294d87f0e1f2d59cdd5e0" /> </OWLClass_SolidMaterial>
6 CONCLUSIONS AND FUTURE WORK In this paper we presented an approach to semantic lifting of unstructured data. As a practical application we applied it to unstructured information from the database of digital library Virtual Encyclopaedia of Bulgarian Iconography [3]. All the work was done in the scope of the SINUS project [5]. Our objective was to enrich the data with additional annotations inferred from natural language textual descriptions that accompany iconographic objects. The later will be used for building and executing semantic queries against the ontology instances in order to infer information that can not be obtained from the original digital library. Our work is also a practical example for upgrading existing legacy system to semantic web level. REFERENCES [1] Marinchev I., Lifting and Lowering the Data from Digital Library "Virtual Encyclopedia of Bulgarian Iconography". Proc of 12th International Conference on Computer Systems and Technologies CompSysTech 2011, Vienna, Austria June 16-17, 2011, ACM ISBN: , pp [2] OWLIM family of semantic repositories [3] Pavlova-Draganova L., V. Georgiev, L. Draganov. Virtual Encyclopaedia of Bulgarian Iconography. Information Technologies and Knowledge, vol.1 (2007), 3, pp [4] Simov K., Z. Peev, M. Kouylekov, A. Simov, M. Dimitrov, A. Kiryakov CLaRK an XML-based System for Corpora Development. In: Proc. of the Corpus Linguistics 2001, pp [5] SINUS Project: Semantic Technologies for Web Services and Technology Enhanced Learning. [6] SparQL query language [7] Staykova K., Agre G., Simov K., Osenova P. Language Technology Support for Semantic Annotation of Iconographic Descriptions. In: Proceedings of the International Workshop "Language Technologies for Digital Humanities and Cultural Heritage", 16 Sept. 2011, Hisar, Bulgaria, ISBN , pp ABOUT THE AUTHOR Assoc. Prof. Ivo Marinchev, PhD, Department of Technologies for Knowledge Management and Processing, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Phone: ( ) , Е-mail: ivo@iinf.bas.bg.
Information Technology for KM
On the Relations between Structural Case-Based Reasoning and Ontology-based Knowledge Management Ralph Bergmann & Martin Schaaf University of Hildesheim Data- and Knowledge Management Group www.dwm.uni-hildesheim.de
More informationtechnische universiteit eindhoven WIS & Engineering Geert-Jan Houben
WIS & Engineering Geert-Jan Houben Contents Web Information System (WIS) Evolution in Web data WIS Engineering Languages for Web data XML (context only!) RDF XML Querying: XQuery (context only!) RDFS SPARQL
More informationHarvesting and Structuring Social Data in Music Information Retrieval
Harvesting and Structuring Social Data in Music Information Retrieval Sergio Oramas Music Technology Group Universitat Pompeu Fabra, Barcelona, Spain sergio.oramas@upf.edu Abstract. An exponentially growing
More informationCOMBINING AND EASING THE ACCESS OF THE ESWC SEMANTIC WEB DATA
STI INNSBRUCK COMBINING AND EASING THE ACCESS OF THE ESWC SEMANTIC WEB DATA Dieter Fensel, and Alex Oberhauser STI Innsbruck, University of Innsbruck, Technikerstraße 21a, 6020 Innsbruck, Austria firstname.lastname@sti2.at
More informationSemantic Modeling with RDF. DBTech ExtWorkshop on Database Modeling and Semantic Modeling Lili Aunimo
DBTech ExtWorkshop on Database Modeling and Semantic Modeling Lili Aunimo Expected Outcomes You will learn: Basic concepts related to ontologies Semantic model Semantic web Basic features of RDF and RDF
More informationUIMA and WebContent: Complementary Frameworks for Building Semantic Web Applications
UIMA and WebContent: Complementary Frameworks for Building Semantic Web Applications Gaël de Chalendar CEA LIST F-92265 Fontenay aux Roses Gael.de-Chalendar@cea.fr 1 Introduction The main data sources
More informationTHE SEMANTIC WEB AND IT`S APPLICATIONS
15-16 September 2011, BULGARIA 1 Proceedings of the International Conference on Information Technologies (InfoTech-2011) 15-16 September 2011, Bulgaria THE SEMANTIC WEB AND IT`S APPLICATIONS Dimitar Vuldzhev
More informationBig Data, Fast Data, Complex Data. Jans Aasman Franz Inc
Big Data, Fast Data, Complex Data Jans Aasman Franz Inc Private, founded 1984 AI, Semantic Technology, professional services Now in Oakland Franz Inc Who We Are (1 (2 3) (4 5) (6 7) (8 9) (10 11) (12
More informationTowards Event Sequence Representation, Reasoning and Visualization for EHR Data
Towards Event Sequence Representation, Reasoning and Visualization for EHR Data Cui Tao Dept. of Health Science Research Mayo Clinic Rochester, MN Catherine Plaisant Human-Computer Interaction Lab ABSTRACT
More informationHEALTH INFORMATION MANAGEMENT ON SEMANTIC WEB :(SEMANTIC HIM)
HEALTH INFORMATION MANAGEMENT ON SEMANTIC WEB :(SEMANTIC HIM) Nasim Khozoie Department of Computer Engineering,yasuj branch, Islamic Azad University, yasuj, Iran n_khozooyi2003@yahoo.com ABSTRACT Information
More informationUnderstanding Web personalization with Web Usage Mining and its Application: Recommender System
Understanding Web personalization with Web Usage Mining and its Application: Recommender System Manoj Swami 1, Prof. Manasi Kulkarni 2 1 M.Tech (Computer-NIMS), VJTI, Mumbai. 2 Department of Computer Technology,
More informationPerformance Analysis, Data Sharing, Tools Integration: New Approach based on Ontology
Performance Analysis, Data Sharing, Tools Integration: New Approach based on Ontology Hong-Linh Truong Institute for Software Science, University of Vienna, Austria truong@par.univie.ac.at Thomas Fahringer
More informationSecure Semantic Web Service Using SAML
Secure Semantic Web Service Using SAML JOO-YOUNG LEE and KI-YOUNG MOON Information Security Department Electronics and Telecommunications Research Institute 161 Gajeong-dong, Yuseong-gu, Daejeon KOREA
More informationA Semantic Web of Know-How: Linked Data for Community-Centric Tasks
A Semantic Web of Know-How: Linked Data for Community-Centric Tasks Paolo Pareti Edinburgh University p.pareti@sms.ed.ac.uk Ewan Klein Edinburgh University ewan@inf.ed.ac.uk Adam Barker University of St
More informationNatural Language to Relational Query by Using Parsing Compiler
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 3, March 2015,
More informationCity Data Pipeline. A System for Making Open Data Useful for Cities. stefan.bischof@tuwien.ac.at
City Data Pipeline A System for Making Open Data Useful for Cities Stefan Bischof 1,2, Axel Polleres 1, and Simon Sperl 1 1 Siemens AG Österreich, Siemensstraße 90, 1211 Vienna, Austria {bischof.stefan,axel.polleres,simon.sperl}@siemens.com
More informationLightweight Data Integration using the WebComposition Data Grid Service
Lightweight Data Integration using the WebComposition Data Grid Service Ralph Sommermeier 1, Andreas Heil 2, Martin Gaedke 1 1 Chemnitz University of Technology, Faculty of Computer Science, Distributed
More informationReason-able View of Linked Data for Cultural Heritage
Reason-able View of Linked Data for Cultural Heritage Mariana Damova 1, Dana Dannells 2 1 Ontotext, Tsarigradsko Chausse 135, Sofia 1784, Bulgaria 2 University of Gothenburg, Lennart Torstenssonsgatan
More informationDLDB: Extending Relational Databases to Support Semantic Web Queries
DLDB: Extending Relational Databases to Support Semantic Web Queries Zhengxiang Pan (Lehigh University, USA zhp2@cse.lehigh.edu) Jeff Heflin (Lehigh University, USA heflin@cse.lehigh.edu) Abstract: We
More informationBig Data and Semantic Web in Manufacturing. Nitesh Khilwani, PhD Chief Engineer, Samsung Research Institute Noida, India
Big Data and Semantic Web in Manufacturing Nitesh Khilwani, PhD Chief Engineer, Samsung Research Institute Noida, India Outline Big data in Manufacturing Big data Analytics Semantic web technologies Case
More informationSEMANTIC WEB BASED INFERENCE MODEL FOR LARGE SCALE ONTOLOGIES FROM BIG DATA
SEMANTIC WEB BASED INFERENCE MODEL FOR LARGE SCALE ONTOLOGIES FROM BIG DATA J.RAVI RAJESH PG Scholar Rajalakshmi engineering college Thandalam, Chennai. ravirajesh.j.2013.mecse@rajalakshmi.edu.in Mrs.
More informationText Mining - Scope and Applications
Journal of Computer Science and Applications. ISSN 2231-1270 Volume 5, Number 2 (2013), pp. 51-55 International Research Publication House http://www.irphouse.com Text Mining - Scope and Applications Miss
More informationSemantic Stored Procedures Programming Environment and performance analysis
Semantic Stored Procedures Programming Environment and performance analysis Marjan Efremov 1, Vladimir Zdraveski 2, Petar Ristoski 2, Dimitar Trajanov 2 1 Open Mind Solutions Skopje, bul. Kliment Ohridski
More informationAccessing XML Documents using Semantic Meta Data in a P2P Environment
Accessing XML Documents using Semantic Meta Data in a P2P Environment Dominic Battré and Felix Heine and AndréHöing University of Paderborn Paderborn Center for Parallel Computing Fürstenallee 11, 33102
More informationNatural Language Processing in the EHR Lifecycle
Insight Driven Health Natural Language Processing in the EHR Lifecycle Cecil O. Lynch, MD, MS cecil.o.lynch@accenture.com Health & Public Service Outline Medical Data Landscape Value Proposition of NLP
More informationDeriving Business Intelligence from Unstructured Data
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 9 (2013), pp. 971-976 International Research Publications House http://www. irphouse.com /ijict.htm Deriving
More informationPersonalization of Web Search With Protected Privacy
Personalization of Web Search With Protected Privacy S.S DIVYA, R.RUBINI,P.EZHIL Final year, Information Technology,KarpagaVinayaga College Engineering and Technology, Kanchipuram [D.t] Final year, Information
More informationWeb Mining. Margherita Berardi LACAM. Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it
Web Mining Margherita Berardi LACAM Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it Bari, 24 Aprile 2003 Overview Introduction Knowledge discovery from text (Web Content
More informationRDF Resource Description Framework
RDF Resource Description Framework Fulvio Corno, Laura Farinetti Politecnico di Torino Dipartimento di Automatica e Informatica e-lite Research Group http://elite.polito.it Outline RDF Design objectives
More informationDe la Business Intelligence aux Big Data. Marie- Aude AUFAURE Head of the Business Intelligence team Ecole Centrale Paris. 22/01/14 Séminaire Big Data
De la Business Intelligence aux Big Data Marie- Aude AUFAURE Head of the Business Intelligence team Ecole Centrale Paris 22/01/14 Séminaire Big Data 1 Agenda EvoluHon of Business Intelligence SemanHc Technologies
More informationD3.1: SYSTEM TEST SUITE
D3.1: SYSTEM TEST SUITE Leroy Finn, David Lewis, Kevin Koidl Distribution: Public Report Federated Active Linguistic data CuratiON (FALCON) FP7- ICT- 2013- SME- DCA Project no: 610879 1 Document Information
More informationUsing NLP and Ontologies for Notary Document Management Systems
Outline Using NLP and Ontologies for Notary Document Management Systems Flora Amato, Antonino Mazzeo, Antonio Penta and Antonio Picariello Dipartimento di Informatica e Sistemistica Universitá di Napoli
More informationSemWeB Semantic Web Browser Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks
SemWeB Semantic Web Browser Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks Melike Şah, Wendy Hall and David C De Roure Intelligence, Agents and Multimedia Group,
More informationA Semantic web approach for e-learning platforms
A Semantic web approach for e-learning platforms Miguel B. Alves 1 1 Laboratório de Sistemas de Informação, ESTG-IPVC 4900-348 Viana do Castelo. mba@estg.ipvc.pt Abstract. When lecturers publish contents
More informationSmart Financial Data: Semantic Web technology transforms Big Data into Smart Data
Smart Financial Data: Semantic Web technology transforms Big Data into Smart Data Insurance Data and Analytics Summit 2013 18 April 2013 David Saul, Senior Vice President & Chief Scientist State Street
More informationSemantic Search in Portals using Ontologies
Semantic Search in Portals using Ontologies Wallace Anacleto Pinheiro Ana Maria de C. Moura Military Institute of Engineering - IME/RJ Department of Computer Engineering - Rio de Janeiro - Brazil [awallace,anamoura]@de9.ime.eb.br
More informationMLg. Big Data and Its Implication to Research Methodologies and Funding. Cornelia Caragea TARDIS 2014. November 7, 2014. Machine Learning Group
Big Data and Its Implication to Research Methodologies and Funding Cornelia Caragea TARDIS 2014 November 7, 2014 UNT Computer Science and Engineering Data Everywhere Lots of data is being collected and
More informationA Tool for Searching the Semantic Web for Supplies Matching Demands
A Tool for Searching the Semantic Web for Supplies Matching Demands Zuzana Halanová, Pavol Návrat, Viera Rozinajová Abstract: We propose a model of searching semantic web that allows incorporating data
More informationCreating an RDF Graph from a Relational Database Using SPARQL
Creating an RDF Graph from a Relational Database Using SPARQL Ayoub Oudani, Mohamed Bahaj*, Ilias Cherti Department of Mathematics and Informatics, University Hassan I, FSTS, Settat, Morocco. * Corresponding
More informationSmart Space for Learning: A Mediation Infrastructure for Learning Services
Smart Space for Learning: A Mediation Infrastructure for Learning Services Bernd Simon Dept. of Information Systems Vienna University of Economics Austria bernd.simon@wuwien.ac.at Michael Sintek German
More informationTraining Management System for Aircraft Engineering: indexing and retrieval of Corporate Learning Object
Training Management System for Aircraft Engineering: indexing and retrieval of Corporate Learning Object Anne Monceaux 1, Joanna Guss 1 1 EADS-CCR, Centreda 1, 4 Avenue Didier Daurat 31700 Blagnac France
More informationSemantic Data Management. Xavier Lopez, Ph.D., Director, Spatial & Semantic Technologies
Semantic Data Management Xavier Lopez, Ph.D., Director, Spatial & Semantic Technologies 1 Enterprise Information Challenge Source: Oracle customer 2 Vision of Semantically Linked Data The Network of Collaborative
More informationAiding the Data Integration in Medicinal Settings by Means of Semantic Technologies
Aiding the Data Integration in Medicinal Settings by Means of Semantic Technologies Vit Novacek 1 Loredana Laera 2 Siegfried Handschuh 1 1 Digital Enterprise Research Institute (DERI) National University
More informationAutomatic Timeline Construction For Computer Forensics Purposes
Automatic Timeline Construction For Computer Forensics Purposes Yoan Chabot, Aurélie Bertaux, Christophe Nicolle and Tahar Kechadi CheckSem Team, Laboratoire Le2i, UMR CNRS 6306 Faculté des sciences Mirande,
More informationSupporting Change-Aware Semantic Web Services
Supporting Change-Aware Semantic Web Services Annika Hinze Department of Computer Science, University of Waikato, New Zealand a.hinze@cs.waikato.ac.nz Abstract. The Semantic Web is not only evolving into
More informationA Framework for Collaborative Project Planning Using Semantic Web Technology
A Framework for Collaborative Project Planning Using Semantic Web Technology Lijun Shen 1 and David K.H. Chua 2 Abstract Semantic web technology has become an enabling technology for machines to automatically
More informationTopics in basic DBMS course
Topics in basic DBMS course Database design Transaction processing Relational query languages (SQL), calculus, and algebra DBMS APIs Database tuning (physical database design) Basic query processing (ch
More informationK@ A collaborative platform for knowledge management
White Paper K@ A collaborative platform for knowledge management Quinary SpA www.quinary.com via Pietrasanta 14 20141 Milano Italia t +39 02 3090 1500 f +39 02 3090 1501 Copyright 2004 Quinary SpA Index
More informationAssociate Professor, Department of CSE, Shri Vishnu Engineering College for Women, Andhra Pradesh, India 2
Volume 6, Issue 3, March 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Special Issue
More informationDatabase preservation toolkit:
Nov. 12-14, 2014, Lisbon, Portugal Database preservation toolkit: a flexible tool to normalize and give access to databases DLM Forum: Making the Information Governance Landscape in Europe José Carlos
More informationSemantic Web Technologies and Data Management
Semantic Web Technologies and Data Management Li Ma, Jing Mei, Yue Pan Krishna Kulkarni Achille Fokoue, Anand Ranganathan IBM China Research Laboratory IBM Software Group IBM Watson Research Center Bei
More informationTowards Semantics-Enabled Distributed Infrastructure for Knowledge Acquisition
Towards Semantics-Enabled Distributed Infrastructure for Knowledge Acquisition Vasant Honavar 1 and Doina Caragea 2 1 Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State
More informationA MEDIATION LAYER FOR HETEROGENEOUS XML SCHEMAS
A MEDIATION LAYER FOR HETEROGENEOUS XML SCHEMAS Abdelsalam Almarimi 1, Jaroslav Pokorny 2 Abstract This paper describes an approach for mediation of heterogeneous XML schemas. Such an approach is proposed
More informationA HUMAN RESOURCE ONTOLOGY FOR RECRUITMENT PROCESS
A HUMAN RESOURCE ONTOLOGY FOR RECRUITMENT PROCESS Ionela MANIU Lucian Blaga University Sibiu, Romania Faculty of Sciences mocanionela@yahoo.com George MANIU Spiru Haret University Bucharest, Romania Faculty
More informationChapter 11 Mining Databases on the Web
Chapter 11 Mining bases on the Web INTRODUCTION While Chapters 9 and 10 provided an overview of Web data mining, this chapter discusses aspects of mining the databases on the Web. Essentially, we use the
More informationSemantic Web Development for Tourism Domain from Conventional Web and Improving the Semantic Search by Providing Different Methods of Categorization
Semantic Web Development for Tourism Domain from Conventional Web and Improving the Semantic Search by Providing Different Methods of Categorization Jayaprabha P Department of Computer Applications, VidyaaVikas
More informationBUSINESS VALUE OF SEMANTIC TECHNOLOGY
BUSINESS VALUE OF SEMANTIC TECHNOLOGY Preliminary Findings Industry Advisory Council Emerging Technology (ET) SIG Information Sharing & Collaboration Committee July 15, 2005 Mills Davis Managing Director
More informationAn Ontology Based Method to Solve Query Identifier Heterogeneity in Post- Genomic Clinical Trials
ehealth Beyond the Horizon Get IT There S.K. Andersen et al. (Eds.) IOS Press, 2008 2008 Organizing Committee of MIE 2008. All rights reserved. 3 An Ontology Based Method to Solve Query Identifier Heterogeneity
More informationA Proposed Framework for Arabic Semantic Annotation Tool
Int. J. Com. Dig. Sys. 3, No. 1, 45-51(2014) 45 International Journal of Computing and Digital Systems A Proposed Framework for Arabic Semantic Tool Ahmed N. El-ghobashy, Gamal M. Attiya, and Hamdy M.
More informationData Integration. May 9, 2014. Petr Kremen, Bogdan Kostov (petr.kremen@fel.cvut.cz, bogdan.kostov@fel.cvut.cz)
Data Integration Petr Kremen, Bogdan Kostov petr.kremen@fel.cvut.cz, bogdan.kostov@fel.cvut.cz May 9, 2014 Data Integration May 9, 2014 1 / 33 Outline 1 Introduction Solution approaches Technologies 2
More informationAn Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them
An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them Vangelis Karkaletsis and Constantine D. Spyropoulos NCSR Demokritos, Institute of Informatics & Telecommunications,
More informationManaging enterprise applications as dynamic resources in corporate semantic webs an application scenario for semantic web services.
Managing enterprise applications as dynamic resources in corporate semantic webs an application scenario for semantic web services. Fabien Gandon, Moussa Lo, Olivier Corby, Rose Dieng-Kuntz ACACIA in short
More informationThe Ontology and Architecture for an Academic Social Network
www.ijcsi.org 22 The Ontology and Architecture for an Academic Social Network Moharram Challenger Computer Engineering Department, Islamic Azad University Shabestar Branch, Shabestar, East Azerbaijan,
More informationCOLINDA - Conference Linked Data
Undefined 1 (0) 1 5 1 IOS Press COLINDA - Conference Linked Data Editor(s): Name Surname, University, Country Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University,
More informationSPARQL UniProt.RDF. Get these slides! Tutorial plan. Everyone has had some introduction slash knowledge of RDF.
SPARQL UniProt.RDF Everyone has had some introduction slash knowledge of RDF. Jerven Bolleman Developer Swiss-Prot Group Swiss Institute of Bioinformatics Get these slides! https://sites.google.com/a/jerven.eu/jerven/home/
More informationSecurity Issues for the Semantic Web
Security Issues for the Semantic Web Dr. Bhavani Thuraisingham Program Director Data and Applications Security The National Science Foundation Arlington, VA On leave from The MITRE Corporation Bedford,
More informationImpelsys: Your Partner for Digital Product Development & Commercialization
Impelsys: Your Partner for Digital Product Development & Commercialization Impelsys is your strategic partner through your workflow process from production to delivery and revenue generation. Publishing
More informationAutomatic Annotation Wrapper Generation and Mining Web Database Search Result
Automatic Annotation Wrapper Generation and Mining Web Database Search Result V.Yogam 1, K.Umamaheswari 2 1 PG student, ME Software Engineering, Anna University (BIT campus), Trichy, Tamil nadu, India
More informationLinked Open Data A Way to Extract Knowledge from Global Datastores
Linked Open Data A Way to Extract Knowledge from Global Datastores Bebo White SLAC National Accelerator Laboratory HKU Expert Address 18 September 2014 Developments in science and information processing
More informationOntology based ranking of documents using Graph Databases: a Big Data Approach
Ontology based ranking of documents using Graph Databases: a Big Data Approach A.M.Abirami Dept. of Information Technology Thiagarajar College of Engineering Madurai, Tamil Nadu, India Dr.A.Askarunisa
More informationSemantic Web Applications
Semantic Web Applications Graham Klyne Nine by Nine http://www.ninebynine.net/ 26 February 2004 Nine by Nine Who am I? Scientific, engineering and networked software systems architecture Motion capture,
More informationAn Ontology Model for Organizing Information Resources Sharing on Personal Web
An Ontology Model for Organizing Information Resources Sharing on Personal Web Istiadi 1, and Azhari SN 2 1 Department of Electrical Engineering, University of Widyagama Malang, Jalan Borobudur 35, Malang
More informationDr. Anuradha et al. / International Journal on Computer Science and Engineering (IJCSE)
HIDDEN WEB EXTRACTOR DYNAMIC WAY TO UNCOVER THE DEEP WEB DR. ANURADHA YMCA,CSE, YMCA University Faridabad, Haryana 121006,India anuangra@yahoo.com http://www.ymcaust.ac.in BABITA AHUJA MRCE, IT, MDU University
More informationTowards the Integration of a Research Group Website into the Web of Data
Towards the Integration of a Research Group Website into the Web of Data Mikel Emaldi, David Buján, and Diego López-de-Ipiña Deusto Institute of Technology - DeustoTech, University of Deusto Avda. Universidades
More informationHow To Make Sense Of Data With Altilia
HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS. ALTILIA turns Big Data into Smart Data and enables businesses to
More informationExam in course TDT4215 Web Intelligence - Solutions and guidelines -
English Student no:... Page 1 of 12 Contact during the exam: Geir Solskinnsbakk Phone: 94218 Exam in course TDT4215 Web Intelligence - Solutions and guidelines - Friday May 21, 2010 Time: 0900-1300 Allowed
More informationEXPLOITING FOLKSONOMIES AND ONTOLOGIES IN AN E-BUSINESS APPLICATION
EXPLOITING FOLKSONOMIES AND ONTOLOGIES IN AN E-BUSINESS APPLICATION Anna Goy and Diego Magro Dipartimento di Informatica, Università di Torino C. Svizzera, 185, I-10149 Italy ABSTRACT This paper proposes
More informationData Integration Hub for a Hybrid Paper Search
Data Integration Hub for a Hybrid Paper Search Jungkee Kim 1,2, Geoffrey Fox 2, and Seong-Joon Yoo 3 1 Department of Computer Science, Florida State University, Tallahassee FL 32306, U.S.A., jungkkim@cs.fsu.edu,
More informationGraph-Based Linking and Visualization for Legislation Documents (GLVD) Dincer Gultemen & Tom van Engers
Graph-Based Linking and Visualization for Legislation Documents (GLVD) Dincer Gultemen & Tom van Engers Demand of Parliaments Semi-structured information and semantic technologies Inter-institutional business
More informationA generic approach for data integration using RDF, OWL and XML
A generic approach for data integration using RDF, OWL and XML Miguel A. Macias-Garcia, Victor J. Sosa-Sosa, and Ivan Lopez-Arevalo Laboratory of Information Technology (LTI) CINVESTAV-TAMAULIPAS Km 6
More informationScalable End-User Access to Big Data http://www.optique-project.eu/ HELLENIC REPUBLIC National and Kapodistrian University of Athens
Scalable End-User Access to Big Data http://www.optique-project.eu/ HELLENIC REPUBLIC National and Kapodistrian University of Athens 1 Optique: Improving the competitiveness of European industry For many
More informationAchille Felicetti" VAST-LAB, PIN S.c.R.L., Università degli Studi di Firenze!
3D-COFORM Mapping Tool! Achille Felicetti" VAST-LAB, PIN S.c.R.L., Università degli Studi di Firenze!! The 3D-COFORM Project! Work Package 6! Tools for the semi-automatic processing of legacy information!
More informationInteractive Dynamic Information Extraction
Interactive Dynamic Information Extraction Kathrin Eichler, Holmer Hemsen, Markus Löckelt, Günter Neumann, and Norbert Reithinger Deutsches Forschungszentrum für Künstliche Intelligenz - DFKI, 66123 Saarbrücken
More informationApproaches of Using a Word-Image Ontology and an Annotated Image Corpus as Intermedia for Cross-Language Image Retrieval
Approaches of Using a Word-Image Ontology and an Annotated Image Corpus as Intermedia for Cross-Language Image Retrieval Yih-Chen Chang and Hsin-Hsi Chen Department of Computer Science and Information
More informationSemantic Knowledge Management System. Paripati Lohith Kumar. School of Information Technology
Semantic Knowledge Management System Paripati Lohith Kumar School of Information Technology Vellore Institute of Technology University, Vellore, India. plohithkumar@hotmail.com Abstract The scholarly activities
More informationExploring the Advances in Semantic Search Engines
Exploring the Advances in Semantic Search Engines Walter Renteria-Agualimpia 1, Francisco J. López-Pellicer 1, Pedro R. Muro- Medrano 1, Javier Nogueras-Iso 1, and F.Javier Zarazaga-Soria 1 1 Computer
More informationCLARIN (in the) UK Tools and Services
CLARIN (in the) UK Tools and Services Johann Petrak (johann.petrak@sheffield.ac.uk) substituting for Wim Peters (w.peters@sheffield.ac.uk), Martin Wynne (martin.wynne@it.ox.ac.uk) 1 Clarin-UK consortium:
More informationAddressing Self-Management in Cloud Platforms: a Semantic Sensor Web Approach
Addressing Self-Management in Cloud Platforms: a Semantic Sensor Web Approach Rustem Dautov Iraklis Paraskakis Dimitrios Kourtesis South-East European Research Centre International Faculty, The University
More informationCHAPTER 6 EXTRACTION OF METHOD SIGNATURES FROM UML CLASS DIAGRAM
CHAPTER 6 EXTRACTION OF METHOD SIGNATURES FROM UML CLASS DIAGRAM 6.1 INTRODUCTION There are various phases in software project development. The various phases are: SRS, Design, Coding, Testing, Implementation,
More informationI. INTRODUCTION NOESIS ONTOLOGIES SEMANTICS AND ANNOTATION
Noesis: A Semantic Search Engine and Resource Aggregator for Atmospheric Science Sunil Movva, Rahul Ramachandran, Xiang Li, Phani Cherukuri, Sara Graves Information Technology and Systems Center University
More informationA Framework of User-Driven Data Analytics in the Cloud for Course Management
A Framework of User-Driven Data Analytics in the Cloud for Course Management Jie ZHANG 1, William Chandra TJHI 2, Bu Sung LEE 1, Kee Khoon LEE 2, Julita VASSILEVA 3 & Chee Kit LOOI 4 1 School of Computer
More informationKeywords Big Data; OODBMS; RDBMS; hadoop; EDM; learning analytics, data abundance.
Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analytics
More informationMobile App Discovery through Conceptual Models
Mobile App Discovery through Conceptual Models Jasmin Brakmic, BSc Supervisor: Prof. Dr. Dimitris Karagiannis Research Group Knowledge Engineering University of Vienna, 24.01.2013 Agenda 1. Motivation
More informationWHITEPAPER MANAGE YOUR PAPER MATERIAL WITHIN YOUR CRM SYSTEM
WHITEPAPER MANAGE YOUR PAPER MATERIAL WITHIN YOUR CRM SYSTEM WHITEPAPER MANAGE YOUR PAPER MATERIAL WITHIN YOUR CRM SYSTEM 2 ABOUT There are two main types of data within the enterprise: structured and
More informationA Platform for Supporting Data Analytics on Twitter: Challenges and Objectives 1
A Platform for Supporting Data Analytics on Twitter: Challenges and Objectives 1 Yannis Stavrakas Vassilis Plachouras IMIS / RC ATHENA Athens, Greece {yannis, vplachouras}@imis.athena-innovation.gr Abstract.
More informationFrom Databases to Natural Language: The Unusual Direction
From Databases to Natural Language: The Unusual Direction Yannis Ioannidis Dept. of Informatics & Telecommunications, MaDgIK Lab University of Athens, Hellas (Greece) yannis@di.uoa.gr http://www.di.uoa.gr/
More informationFolksonomies versus Automatic Keyword Extraction: An Empirical Study
Folksonomies versus Automatic Keyword Extraction: An Empirical Study Hend S. Al-Khalifa and Hugh C. Davis Learning Technology Research Group, ECS, University of Southampton, Southampton, SO17 1BJ, UK {hsak04r/hcd}@ecs.soton.ac.uk
More informationChapter 6. Attracting Buyers with Search, Semantic, and Recommendation Technology
Attracting Buyers with Search, Semantic, and Recommendation Technology Learning Objectives Using Search Technology for Business Success Organic Search and Search Engine Optimization Recommendation Engines
More informationApplying semantics in the environmental domain: The TaToo project approach
EnviroInfo 2011: Innovations in Sharing Environmental Observations and Information Applying semantics in the environmental domain: The TaToo project approach Giuseppe Avellino 1, Tomás Pariente Lobo 2,
More informationXOP: Sharing XML Data Objects through Peer-to-Peer Networks
22nd International Conference on Advanced Information Networking and Applications XOP: Sharing XML Data Objects through Peer-to-Peer Networks Itamar de Rezende, Frank Siqueira Department of Informatics
More information