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Applications of Semantic Web

Applications of Semantic Web. Lin, Shih-Jui and Chien, Lee-Feng Institute of Information Science Academia Sinica. AI Machine Learning. Language Technology NLP/IE. Semantic Web. Data Mining. Information Retrieval. Knowledge Management. Agent Web Service.

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Applications of Semantic Web

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  1. Applications of Semantic Web Lin, Shih-Jui and Chien, Lee-Feng Institute of Information Science Academia Sinica

  2. AI Machine Learning Language Technology NLP/IE Semantic Web Data Mining Information Retrieval Knowledge Management Agent Web Service Semantic Web and Related Fields

  3. Semantic Web and Related Fields AI Machine Learning Language Technology NLP/IE Semantic Web Data Mining Information Retrieval Knowledge Management Agent Web Service

  4. Building Semantic Web • Ontology • Building • repositories of terms and their relationships (LT) • ontology generation (ML) • Mapping and merging • knowledge of language, terms (LT) • mapping and merging (ML) • Knowledge base • Adding instances into KB • structure/content mining (DM) • text analysis and extract values of attributes (NLP, IE, ML) • Document • Semantic annotation • association between words and annotations (DM, ML)

  5. Semantic Web and Related Fields AI Machine Learning Language Technology NLP/IE Semantic Web Data Mining Information Retrieval Knowledge Management Agent Web Service

  6. Using Semantic Web • Language technology • Text corpora with semantics • Data mining • Content/structure mining from semantic web pages • Usage mining from user’s activities on semantic web

  7. Using Semantic Web • Information retrieval • Metadata search • Topic-based search • Knowledge management • Acquire, maintain, access knowledge • Agent technology / web services • DAML-S • RETSINA calendar agent

  8. Application I Information Retrieval

  9. Information Retrieval • Current search • Search on Semantic Web • Metadata search • Project: HOWLIR • Topic-based search • Project: TAP

  10. Current Search • Is keyterm-based search (e.g., Google) • Full text indexing • Page authority (link analysis) • Page popularity (user’s click) • Problems • Not specific • Data in pages have no semantic annotations • Yo-yo Ma’s most recent CD • No topic disambiguation • Documents with different topics mix together • Yo-yo Ma’s CDs, concerts, biography, gossips…,

  11. Information Extraction • Wrapper • Specific web sites • Structured documents • Heuristic extraction • Information extraction • Unstructured documents • Natural language analysis • Values for specific attributes • Problems • Not flexible • Current web provides little metadata • No topic disambiguation

  12. XML • Metadata • <Person> <Name> Yo-yo Ma </Name> <CD>Inspired by Bach</CD> </Person> XML (Extensible Markup Language) Adapted from Dieter Fensel

  13. RDF/RDFS • Pre-defined modeling primitives • The base of metadata search RDFS metadata search RDF (Resource Description Framework) XML (Extensible Markup Language) Adapted from Dieter Fensel

  14. musician concert CD … price time … Ontology • Sharable specifications of interesting topics • The base of topic-based search Ontology topic-based search RDFS metadata search RDF (Resource Description Framework) XML (Extensible Markup Language) Adapted from Dieter Fensel

  15. Search on Semantic Web • Metadata search • To increase precision and flexibility • Topic-based search • To help contextualize queries and overlay results in terms of a knowledge base

  16. Metadata Search • To annotate metadata on documents (XML/RDF/RDFS) • To index both full text and metadata • To retrieve documents according to both text and metadata (Hybrid IR) • e.g., HOWLIR IR system (UMBC, John Hopkins)

  17. Text man-built auto-annotate NLP/IE/DM/ML Indexed text & metadata query result HOWLIR • To extract terms from documents via AutoTextTM • To learn metadata by the statistical associations between metadata and text in annotated documents • To generate annotations in RDF/DAML • To retrieve documents according to text and metadata

  18. Topic-based Search • To help contextualize queries and overlay results in terms of a knowledge base • E.g.TAP (IBM, Stanford)

  19. TAP KB UDDI++ Musician whose genre is ClassicalMusic, First name is … Who has - concert dates? - discography? - auctions? - bio? For musician whose Search Front End “Yo Yo Ma” Caching & Buffering Auctions for … Concert Dates for Musician whose … Bio for … Discography for … AllMusic TicketMaster EBay CDNow

  20. TAP KB • Ontology and instances in specific domains (music, sport, etc.) • Manual editing • Mining free data sources on the Web • Reading news articles and automatically identifying new musicians, athletes, etc. • Currently covers about 20% of queries • In RDF, DAML+OIL format • Browse the KB at TAP site

  21. Summary of IR • Metadata search • HOWLIR • Topic-based search • TAP

  22. Application II Knowledge Management

  23. Knowledge Management • What is KM? • KM in a company • KM on Semantic Web • Project: Ontoknowledge

  24. What is KM? • Acquiring knowledge • Gather • Organize • Maintaining knowledge • Represent • Update • Accessing knowledge • Search • Visualize/browse • Share

  25. KM in a Company • To organize, maintain, and access the knowledge and experiences effectively (organization memory) • To share documents among different departments • To reduce the overhead of training • To reduce the cost of customer services • To reduce labor force

  26. KM on Semantic Web • Semantic web provides infrastructure for KM • Acquiring knowledge: • Ontology building • KB building • Maintaining knowledge: • Represented in RDF/DAML/OIL • Accessing knowledge: • Intelligent search • Ontology-based visualization • Ontology-based sharing

  27. Ontoknowledge • A project developed by • Academic groups • Free University Amsterdam • University of Karlsruhe • Companies • British Telecom (call center) • Swiss Life (insurance company) • Enersearch (virtual enterprises) • CognIT, Aidministrator, Ontotext Lab

  28. OntoShare User RQL RDF Ferret Spectacle Knowledge Engineer OntoEdit OIL-Core OMM LINRO Sesame OIL-Core ontologyrepository Annotated Data Repository RDF RDF pers05 731 par05 car tel about OntoWrapper OntoExtract Data Repository (external) This text is about cars even though you can’t read it Architecture of Ontoknowledge acquire

  29. Manual Ontology Building and Instantiation • OntoEdit • A tool for building an ontology and instances manually

  30. OntoShare User RQL RDF Ferret Spectacle Knowledge Engineer access OntoEdit OIL-Core OMM LINRO Sesame OIL-Core ontologyrepository Maintain Annotated Data Repository RDF RDF pers05 731 par05 car tel about OntoWrapper OntoExtract Data Repository (external) This text is about cars even though you can’t read it Architecture of Ontoknowledge acquire

  31. Visualization • Spectacle: ontology-based knowledge presentation

  32. Case Studies • Swiss Life • British Telecom

  33. Swiss Life • IAS (International Accounting Standard) • Searching a large document on the Intranet  OntoExtract • Learning ontology from documents • Assisting in reformulating user’s query

  34. Swiss Life • Management of skills of employees  Annotation of employees’ homepages • Skills, education, job functions • Ontology of skills • Comparing, querying employees’ skills • Find out the most experienced employee at fire insurance for chemistry factories

  35. British Telecom • CRM (customer relationship management) • Cost increases 20% every year  OntoShare • Disseminating customer handling rules and best practice • Identifying customers’ problems by search/browse the ontology • Keeping track of customer's needs, interests and preferences

  36. Summary of KM • Ontology-based KM • Acquiring knowledge: • Ontology building • KB building • Maintaining knowledge: • Represented in RDF/DAML/OIL • Accessing knowledge: • Intelligent search • Ontology-based visualization • Ontology-based sharing • Ontoknowledge and case studies

  37. Application III Web Services

  38. Web Services • Current web services • Semantic Web services • DAML-S • Project: RETSINA calendar agent

  39. TowardInt’l Semantic Web Conference To attend ISWC 2003 in Florida…..

  40. Current Web Services • A user has to • Find the services (e.g. by Google) • Find the web sites of hotels and airline • Composite the services to achieve his goal • Book tickets and hotels • Invoke the services • Fill out the forms in each site • Monitor the execution of services • Is the transaction done? • Consider his constraints and preferences • Cheaper hotels but better airline Current Web

  41. Semantic Markup Service Markup User Markup Semantic Web Services • Agent-based technology • To automate • Service discovery • Service invocation • Service selection and composition • Service execution monitoring • User constraints and preferences

  42. DAML-S A Framework Adapted from IEEE Intelligent Systems

  43. DAML-S • DARPA Agent Markup Language for Services • A DAML+OIL ontology/language for describing properties and capabilities of web services • DAML-S Coalition • CMU, Stanford, Yale, BBN, Nokia, SRI

  44. DAML+OIL (Ontology) RDFS (RDF Schema) RDF (Resource Description Framework) XML (Extensible Markup Language) DAML-S in the Cake Agent-based technology DAML-S (Services) Adapted from AAAI

  45. Upper Ontology of Services Adapted from AAAI

  46. Upper Ontology of Services Adapted from AAAI

  47. Upper Ontology of Services Adapted from AAAI

  48. DAML-S / WSDL Grounding • Web Services Description Language • Authored by IBM, Ariba, Microsoft • Focus of W3C Web Services Description WG • Commercial momentum • Specifies message syntax accepted/generated by communication ports • Bindings to popular message/transport standards (SOAP, HTTP, MIME) • Abstract “types”; extensibility elements • Complementary with DAML-S Adapted from AAAI

  49. (Some) Related Work Related Industrial Initiatives • UDDI • ebXML • WSDL • .Net • XLANG • Biztalk, e-speak, etc These XML-based initiatives are largely complementary to DAML-S. DAML-S aims to build on top of these efforts enabling increased expressiveness, semantics, and inference enabling automation. Related Academic Efforts • Process Algebras (e.g., Pi Calculus) • Process Specification Language (Hoare Logic, PSL) • Planning Domain Definition Language (PDDL) • Business Process Modeling (e.g., BMPL) • OntoWeb Process Modeling Effort Adapted from AAAI

  50. Tools and Applications DAML-S is just another DAML+OIL ontology  All the tools & technologies for DAML+OIL are relevant Some DAML-S Specific Tools and Technologies: Discovery, Matchmaking, Agent Brokering: CMU, SRI (OAA), Stanford KSL Automated Web Service Composition: Stanford KSL, BBN/Yale/Kestrel, CMU, MIT, Nokia, SRI DAML-S Editor: Stanford KSL, SRI, CMU (profiles), Manchester Process Modeling Tools & Reasoning: SRI, Stanford KSL Service Enactment /Simulation: SRI, Stanford KSLFormal Specification of DAML-S Operational/Execution Semantics: CMU, Stanford KSL, SRI Adapted from AAAI

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