Applications of Semantic Web Lin, Shih-Jui and Chien, Lee-Feng Institute of Information Science Academia Sinica
Semantic Web and Related Fields AI Machine Learning Language Technology NLP/IE Information Retrieval Semantic Web Data Mining Knowledge Management Agent Web Service
Semantic Web and Related Fields AI Machine Learning Language Technology NLP/IE Information Retrieval Semantic Web Data Mining Knowledge Management Agent Web Service
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)
Semantic Web and Related Fields AI Machine Learning Language Technology NLP/IE Information Retrieval Semantic Web Data Mining Knowledge Management Agent Web Service
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
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
Application I Information Retrieval
Information Retrieval • Current search • Search on Semantic Web - Metadata search x. Project: HOWLIR - Topic-based search x. Project: TAP
Current Search • Is keyterm-based search (e. g. , Google) - Full text indexing - Page authority (link analysis) - Page popularity (user’s click) • Problems - Not specific x. Data in pages have no semantic annotations x. Yo-yo Ma’s most recent CD - No topic disambiguation x. Documents with different topics mix together x. Yo-yo Ma’s CDs, concerts, biography, gossips…,
Information Extraction • Wrapper x. Specific web sites x. Structured documents x. Heuristic extraction • Information extraction x. Unstructured documents x. Natural language analysis x. Values for specific attributes • Problems - Not flexible Current web provides little metadata - No topic disambiguation
XML • Metadata - <Person> <Name> Yo-yo Ma </Name> <CD>Inspired by Bach</CD> </Person> XML (Extensible Markup Language) Adapted from Dieter Fensel
RDF/RDFS • Pre-defined modeling primitives • The base of metadata search RDFS RDF (Resource Description Framework) XML (Extensible Markup Language) Adapted from Dieter Fensel
Ontology • Sharable specifications of interesting topics • The base of topic-based search metadata search musician CD … time concert … price Ontology RDFS RDF (Resource Description Framework) XML (Extensible Markup Language) Adapted from Dieter Fensel
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
Metadata Search • To annotate metadata on documents (XML/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)
HOWLIR - To extract terms from documents via Auto. Text. 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 auto-annotate NLP/IE/DM/ML Indexed text & metadata man-built query result
Topic-based Search • To help contextualize queries and overlay results in terms of a knowledge base • E. g. TAP (IBM, Stanford)
TAP KB UDDI++ Musician whose genre is Classical. Music, First name is … Search Front End “Yo Yo Ma” Who has - concert dates? - discography? - auctions? - bio? For musician whose Caching & Buffering Auctions for … Bio for … Concert Dates for Musician whose … Discography for … EBay CDNow All. Music Ticket. Master
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
Summary of IR • Metadata search - HOWLIR • Topic-based search - TAP
Application II Knowledge Management
Knowledge Management • • What is KM? KM in a company KM on Semantic Web Project: Ontoknowledge
What is KM? • Acquiring knowledge - Gather - Organize • Maintaining knowledge - Represent - Update • Accessing knowledge - Search - Visualize/browse - Share
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
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
Ontoknowledge • A project developed by - Academic groups x. Free University Amsterdam x. University of Karlsruhe - Companies x. British Telecom (call center) x. Swiss Life (insurance company) x. Enersearch (virtual enterprises) x. Cogn. IT, Aidministrator, Ontotext Lab
Architecture of Ontoknowledge Onto. Share RDF Ferret Spectacle RQL User Knowledge Engineer Onto. Edit OIL-Core OMM LINRO Sesame OIL-Core ontology repository acquire Annotated Data Repository RDF pers 05 RDF tel Onto. Wrapper 731 par 05 Data Repository (external) about car Onto. Extract This text is about cars even though you can’ t read it
Manual Ontology Building and Instantiation • Onto. Edit - A tool for building an ontology and instances manually
Architecture of Ontoknowledge Onto. Share RDF Ferret Spectacle RQL User Knowledge Engineer Onto. Edit OIL-Core access OMM LINRO Maintain Sesame OIL-Core ontology repository acquire Annotated Data Repository RDF pers 05 RDF tel Onto. Wrapper 731 par 05 Data Repository (external) about car Onto. Extract This text is about cars even though you can’ t read it
Visualization • Spectacle: ontology-based knowledge presentation
Case Studies • Swiss Life • British Telecom
Swiss Life • IAS (International Accounting Standard) - Searching a large document on the Intranet Onto. Extract - Learning ontology from documents - Assisting in reformulating user’s query
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
British Telecom • CRM (customer relationship management) - Cost increases 20% every year Onto. Share - Disseminating customer handling rules and best practice - Identifying customers’ problems by search/browse the ontology - Keeping track of customer's needs, interests and preferences
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
Application III Web Services
Web Services • • Current web services Semantic Web services DAML-S Project: RETSINA calendar agent
Toward Int’l Semantic Web Conference To attend ISWC 2003 in Florida…. .
Current Web Services • A user has to - Find the services (e. g. by Google) x. Find the web sites of hotels and airline - Composite the services to achieve his goal x. Book tickets and hotels - Invoke the services x. Fill out the forms in each site - Monitor the execution of services x. Is the transaction done? - Consider his constraints and preferences x. Cheaper hotels but better airline Current Web
Semantic Web Services • Agent-based technology • To automate - Service discovery Service invocation Service selection and composition Service execution monitoring User constraints and preferences Semantic Markup Service Markup User Markup
A Framework DAML-S Adapted from IEEE Intelligent Systems
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
DAML-S in the Cake Agent-based technology DAML-S (Services) DAML+OIL (Ontology) RDFS (RDF Schema) RDF (Resource Description Framework) XML (Extensible Markup Language) Adapted from AAAI
Upper Ontology of Services Adapted from AAAI
Upper Ontology of Services Adapted from AAAI
Upper Ontology of Services Adapted from AAAI
DAML-S / WSDL Grounding • Web Services Description Language - Authored by IBM, Ariba, Microsoft Focus of W 3 C 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
(Some) Related Work Related Industrial Initiatives • UDDI • eb. XML • WSDL • . Net • XLANG • Biztalk, e-speak, etc These XML-based initiatives are largely complementary to 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) • Onto. Web Process Modeling Effort Adapted from AAAI
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
RETSINA • Multi-agent system • Developed by Katia Sycara et. al. (CMU) • http: //www. daml. ri. cmu. edu/site/projects/RDFCalendar/
RETSINA Calendar Agents • Meeting scheduling agents - Meetings have several properties including: x. Time/Duration x. Attendee Information x. Location x. Description • Functions: - Allow user to browse schedule and events - Support meeting scheduling x. Agents negotiate possible meeting times based on user’s schedule and preferences - Import schedules into MS Outlook
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
Services Beyond RETSINA • Cooperation with other agents on Semantic web - Reminding upcoming registration or submission deadlines - Booking a flight to a conference
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) DAML+OIL (Ontology) RDFS (RDF Schema) RDF (Resource Description Framework) XML (Extensible Markup Language) Adapted from AAAI
Summary AI Machine Learning Language Technology NLP/IE Information Retrieval Semantic Web Data Mining Knowledge Management Agent Web Service
Summary AI Machine Learning Language Technology NLP/IE Information Retrieval Semantic Web Data Mining Knowledge Management Agent Web Service
Summary AI Machine Learning Language Technology NLP/IE Information Retrieval Semantic Web Data Mining Knowledge Management Agent Web Service
Summary AI Machine Learning Language Technology Semantic Web Data Mining Information Retrieval Knowledge Management Agent Web Service Metadata search Topic-based search Ontology-based KM Ontoknowledge DAML-S RETSINA NLP/IE
Q&A Thank you!
References • Introduction to Semantic Web - http: //www. cs. vu. nl/~dieter/ftp/slides/kcap. pdf • Official sites: - http: //www. w 3. 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/
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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