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Semantic Web

Semantic Web. Jayasree, Jenny, Raj, Sunil. Agenda. What is Semantic Web vs Semantic History of Semantic Web Current Research Areas (eg OWL, RDF, reasoning engines, query languages) Applications (eg. Medical Domain) Architecture/System components Challenges Future directions Reference.

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Semantic Web

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  1. Semantic Web Jayasree, Jenny, Raj, Sunil

  2. Agenda • What is Semantic Web vs Semantic • History of Semantic Web • Current Research Areas (eg OWL, RDF, reasoning engines, query languages) • Applications (eg. Medical Domain) • Architecture/System components • Challenges • Future directions • Reference

  3. What is Semantics vs. Semantic Web? • Semantics: • focuses on the relation between signifiers, such as words, phrases, signs and symbols, and what they stand for. “Basically, what the sentence really means” • Semantic Web: • The Semantic Web is data that enables machines to understand the meaning of information on the Internet. It extends the network human-readable web pages by inserting machine-readable metadata and how they are related to each other. This enables automated agents to access the Web more intelligently and perform tasks on behalf of the user “Using metadata to add (and extract) meaning” l

  4. Understanding the Semantics • Computers don’t understand the website they are showing to us • They may understand the syntax, the semantics is not. • If computers can understand what we are looking for, they can help us search better • From passively helping us, to actively helping us • Understanding the meaning behind the Webpage l

  5. What will Semantic Web Understand? • The computer will learn them and understand how they interact with each other. • Person • Place • Event • Things • Currently it is dependant on Keywords

  6. History of Semantic Web 1989 by Sir Tim Berners-Lee

  7. Current Research Areas Representation Reasoning Engines Query Languages

  8. Web Ontology Language - RDFS RDF Schema • RDF is a data model for objects and relations between them • RDF Schema (RDFS) is a vocabulary description language based on XML and logic programming. • Describes properties and classes of RDF resources • Provides semantics for generalization hierarchies of properties and classes

  9. Web Ontology Language - OWL OWL • A richer ontology language based on description logic. More expressive languages than RDF Schema. • Is the current Web standard • Relations between classes • e.g., disjointness • Cardinality restrictions • e.g. “exactly one” • Boolean expressions • Richer typing of properties • characteristics of properties (e.g., symmetry)

  10. Web Ontology Language - DAML+OIL • DAML was funded by US government. US Defense Advanced Research Project Agency launched the DARPA Agent Markup Language to make web content more accessible. • Ontology Inference Layer (OIL), a description logic. • DAML+OIL • developed by a joint committee from the US and the European Union (IST) in the context of DAML, a DARPA project for allowing semantic interoperability in XML. • DAML+OIL is built on RDF(S), extends with arbitrary data types from XML Schema type system.

  11. More Ontology Languages • SHOE – Simple HTML Ontology Extension (University of Maryland) • OML – Ontology Markup Language (University of Washington) is partially based on SHOE, provides serialization of SHOE • XOL – Ontology Exchange Language (US bioinformatics community) for exchange of ontology definitions among different software systems.

  12. XOL RDFS SHOE OML OIL DAML+OIL Expressiveness of languages Heavyweight

  13. Querying Language – SPARQL • SPARQL is an RDF query language.

  14. JENA Semantic Web Toolkit • Create and populate RDF data models in Java applications. • Persist them to database • Query the models programmatically • Interface with Ontology engine • Interface with SPARQL

  15. Reasoning Engine – Pellet OWL Reasoner for Java • An open-source Java based OWL DL reasoner. • Dual licensing model • Open source applications - can be used under the terms of AGPL version 3 license • Closed source/commercial applications – can be used under alternative license terms. • Execute SPARQL • http://clarkparsia.com/pellet/

  16. Applications Medical Domain Product recommendations in eCommerce?

  17. A Medical Information Management System(MIMS) • Uses Semantic Web Technologies • Diagnosis method of “dementia” • Data items are changed as research progress.

  18. Motivation for MIMSChallenges faced by Medical researchers in developing a method to diagnose dementia • Spends lot of time analyzing medical data in: • questionnaire survey, • metadata, • MMSE (Mini-Mental State Examination) data, • MRI (Magnetic Resonance Imaging) data, • MEG (Magnetoencephalography) data, and • physical checkup data. Some of them are saved as JPEG format, DICOM (Digital Imaging and Communications in Medicine) format [1], • Microsoft Excel format, Microsoft • PowerPoint format and • Adobe PDF format. • Stored in network file folders; not easy to retrieve • Complicated retrieval needs • MMSE> 20, how to retrieve this info?

  19. Semantic Web – The solution • A major hurdle of the Semantic Web is the creation of metadata. • The metadata creation is a tedious and laborious process for the researchers. • However, it is vital for articulating the medical data.

  20. MIMS – Essential components • (1) A pluggable metadata extractor Metadata extraction mechanism that given as a plug-in which automatically generates a set of metadata from medical data files. • (2) An RDFView A semantic Web retrieval mechanism which provides Web application programming interfaces created from SPARQL templates dynamically. • (3) A representation mechanism A method to display a result of the semantic Web retrieval service.

  21. MIMS(Contd…) The basic metadata schema using RDF graph An overview of the pluggable metadata extractor An RDF model for DICOM image files An overview of RDFView

  22. Overview of MIMS

  23. Many other medical applications • SemMed Applying Semantic Web to Medical Recommendation Systems • Patient-Oriented Systems

  24. Architecture/System components • Logic layer • enhance ontology languages further • application-specific declarative knowledge • Proof layer • Proof generation, exchange, validation • Trust layer • Digital signatures • recommendations, rating agencies. • XML layer • Syntactic basis • RDF layer • RDF basic data model for facts • RDF Schema • Ontology layer • More expressive languages • Current Web standard: OWL

  25. Semantic Web Architecture • A complete database architecture, not only an application program. • Semantic web architecture combines a two-step process. • First, a Semantic Web database is created from unstructured text documents. • And, then Semantic Web applications run on the Semantic Web database; not the original source documents. • The Semantic Web architecture is created by first converting text files to XML • Then analysis performed around these with a semantic processor. This process understands the meaning of the words and grammar of the sentence, and also the semantic relationships of the context. • These meanings and relationships are then stored in a Semantic web database. [13]

  26. Semantic Web Challenges Classification of semantic web challenges: • Implementation • Usage • General

  27. Semantic Web Challenges

  28. General Challenges Multilingualism – to implement the same page in several languages with or without caching. Social Hurdles – acceptability at management level. Treated as another mumbo jumbo.

  29. Execution Challenges Visualization Scalability Services Trust

  30. Implementation challenges • Ontological Modeling • Ontologies are key elements of semantic web. • Since these are extensions into a different domain of knowledge, creation and development becomes extremely hard. • Manual construction of Ontologies is a time consuming operation.

  31. Implementation Challenges • Ontology Engineering • Construction • Alignment • Merging

  32. Implementation Challenges • Annotations and Metadata • Annotation of web contents is difficult. • Though it exists, it is not consistent.

  33. Construction Challenges Identifying and categorizing hierarchies of concepts. Categorizing documents. Classifying concepts. Finding non-taxonomic relationship between documents. Finding interrelated terms.

  34. Mapping Challenges Finding proper metrics for mapping. Creating new classes.

  35. Merging Challenges Finding proper candidates for merging. Finding proper metrics for merging once the candidates are identified.

  36. Future Directions • “Semantic Web” • Used in Facebook for establishing relationships and connections. • Used in Learning Management Systems to classify documents uniformly across all LMS. • Use in Medical record management systems. • More usages envisaged.

  37. References • John Davies, “Semantic Web” presentation http://www.keapro.net/sekt/SemWebTutorialGeneralJD.ppt • N. Henze and D. Krause, “Semantic Web Introduction”, http://www.kbs.uni-hannover.de/Lehre/semweb06/all_in_one.pdf • Charla Woodbury, David Embley, “Family History Research on the Semantic Web: Building a Semantic Prototype for Danish Research”, http://fht.byu.edu/prev_workshops/workshop05/FHTCD/session1/s1-CharlaWoodbury_SemanticWeb.pdf • Deborah L. McGinnes et al, “DAML+OIL: An Ontology Language for the Semantic Web”, IEEE Intelligent Systems Journal, 2002 • Asuncion Gomez-Perez and oscar Corcho, “Ontology Languages for the Semantic Web”, IEEE Intelligent Systems Journal, 2002 • Mahmoud Barhamgi et al, “A framework for Data and Web Services semantic mediation in Peer-to-Peer based Medical Information System”, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS’06), 2006 • Masaharu Hayashi et al, “A Medical Information Management System Using the Semantic Web Technology”, Fourth International Conference on Networked Computing and Advanced Information Management, 2008 • MohammadReza Keyvanpour et al, “Comparative Classification of Semantic Web Challenges and Data Mining Techniques”, 2009 International Conference on Web Information Systems and Mining • Kees van der Sluijs, “Semantic Web Applications” presentation, http://wwwis.win.tue.nl/~ksluijs/material/wis-semwebapplications-class.ppt • Grigoris Antonious, Frank van Harmelen, “A Semantic Web Primer”, 2008, http://kianian.com/userfiles/semantic-web-primer.pdf • Philip McCarthy, “Introduction to Jena”, https://www.ibm.com/developerworks/java/library/j-jena/ • SPARQL Query Language for RDF, http://www.w3.org/TR/rdf-sparql-query/ • Semantic Web Architecture and Applications, http://www.oss.net

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