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

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Presentation on theme: "Applications of Semantic Web Lin, Shih-Jui and Chien, Lee-Feng Institute of Information Science Academia Sinica."— Presentation transcript:

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

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

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

4 Building Semantic Web Ontology -Building xrepositories of terms and their relationships (LT) xontology generation (ML) -Mapping and merging xknowledge of language, terms (LT) xmapping and merging (ML) Knowledge base -Adding instances into KB xstructure/content mining (DM) xtext analysis and extract values of attributes (NLP, IE, ML) Document -Semantic annotation xassociation between words and annotations (DM, ML)

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

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 Current search Search on Semantic Web -Metadata search xProject: HOWLIR -Topic-based search xProject: 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 xData in pages have no semantic annotations xYo-yo Ma’s most recent CD -No topic disambiguation xDocuments with different topics mix together xYo-yo Ma’s CDs, concerts, biography, gossips…,

11 Information Extraction Wrapper xSpecific web sites xStructured documents xHeuristic extraction Information extraction xUnstructured documents xNatural language analysis xValues for specific attributes Problems -Not flexible  Current web provides little metadata -No topic disambiguation

12 XML Metadata - Yo-yo Ma Inspired by Bach XML (Extensible Markup Language) Adapted from Dieter Fensel

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

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

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 HOWLIR -To extract terms from documents via AutoText TM -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 Text Indexed text & metadata man-built auto-annotate NLP/IE/DM/ML query result

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

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

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 siteKB

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

22 Application II Knowledge Management

23 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: x Ontology building x KB building -Maintaining knowledge: x Represented in RDF/DAML/OIL -Accessing knowledge: x Intelligent search x Ontology-based visualization x Ontology-based sharing

27 Ontoknowledge A project developed by -Academic groups xFree University Amsterdam xUniversity of Karlsruhe -Companies xBritish Telecom (call center) xSwiss Life (insurance company) xEnersearch (virtual enterprises) xCognIT, Aidministrator, Ontotext Lab

28 Architecture of Ontoknowledge     ’   tel pers05731 about par05car RDF Annotated Data Repository Data Repository (external) OIL-Core ontology repository RDF Ferret User RQL OIL-Core OntoEdit Spectacle OntoExtractOntoWrapper OntoShare Knowledge Engineer Sesame OMMLINRO acquire

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

30 Architecture of Ontoknowledge     ’   tel pers05731 about par05car RDF Annotated Data Repository Data Repository (external) OIL-Core ontology repository RDF Ferret User RQL OIL-Core OntoEdit Spectacle OntoExtractOntoWrapper OntoShare Knowledge Engineer Sesame OMMLINRO access Maintain 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: x Ontology building x KB building -Maintaining knowledge: x Represented in RDF/DAML/OIL -Accessing knowledge: x Intelligent search x Ontology-based visualization x Ontology-based sharing Ontoknowledge and case studies

37 Application III Web Services

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

39 Toward Int’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) xFind the web sites of hotels and airline -Composite the services to achieve his goal xBook tickets and hotels -Invoke the services xFill out the forms in each site -Monitor the execution of services xIs the transaction done? -Consider his constraints and preferences xCheaper hotels but better airline Current Web

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

42 A Framework DAML-S 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-S in the Cake Agent-based technology DAML-S (Services) XML (Extensible Markup Language) RDF (Resource Description Framework) RDFS (RDF Schema) DAML+OIL (Ontology) 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 KSL Formal Specification of DAML-S Operational/Execution Semantics: CMU, Stanford KSL, SRI Adapted from AAAI

51 RETSINA Multi-agent system Developed by Katia Sycara et. al. (CMU) http://www.daml.ri.cmu.edu/site/projects/RDFCalendar/

52 RETSINA Calendar Agents Meeting scheduling agents -Meetings have several properties including: xTime/Duration xAttendee Information xLocation xDescription Functions: -Allow user to browse schedule and events -Support meeting scheduling xAgents negotiate possible meeting times based on user’s schedule and preferences -Import schedules into MS Outlook

53 RETSINA Semantic Web Calendar Agents Use RDF to represent schedules and events -Event concepts can refer to existing concepts on Semantic web Support additional actions based on available information -Email or visit web page Support agent discovery (DAML-S) to locate other agents

54 Services Beyond RETSINA Cooperation with other agents on Semantic web -Reminding upcoming registration or submission deadlines -Booking a flight to a conference

55 Summary of Web Services Semantic web makes it possible to automate web services by agent-based technology Agent-based Technology (e.g.RETSINA) DAML-S (Services) XML (Extensible Markup Language) RDF (Resource Description Framework) RDFS (RDF Schema) DAML+OIL (Ontology) Adapted from AAAI

56 Summary Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management

57 Summary Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management

58 Summary Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management

59 Summary Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management Metadata search Topic-based search DAML-S RETSINA Ontology-based KM Ontoknowledge

60 Q & A Thank you!

61 References Introduction to Semantic Web -http://www.cs.vu.nl/~dieter/ftp/slides/kcap.pdf Official sites: -http://www.w3.org/2001/sw/ -http://www.semanticweb.org/ DAML-S -http://www.daml.org/services/ Projects: -Ontoknowledge: http://www.semanticweb.org/ -TAP: http://tap.stanford.edu -RETSINA : http://www.daml.ri.cmu.edu/site/projects/RDFCalendar/

62 Conferences Semantic web -ISWC (International Semantic Web Conference) -WWW Conference LT -COLING AI -Ontologies and Semantic Web Workshop (AAAI) -Language Resources Meets Semantic Web Workshop (AAAI) DM -Semantic Web Mining Workshop (ECML/PKDD) KM -Knowledge Technologies Conference

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