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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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Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management Semantic Web and Related Fields
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Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management Semantic Web and Related Fields
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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)
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Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management Semantic Web and Related Fields
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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
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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
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Application I Information Retrieval
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Current search Search on Semantic Web -Metadata search xProject: HOWLIR -Topic-based search xProject: TAP
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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…,
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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
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XML Metadata - Yo-yo Ma Inspired by Bach XML (Extensible Markup Language) Adapted from Dieter Fensel
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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
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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
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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
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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)
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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
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To help contextualize queries and overlay results in terms of a knowledge base E.g. TAP (IBM, Stanford)TAP Topic-based Search
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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
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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
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Summary of IR Metadata search -HOWLIR Topic-based search -TAP
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Application II Knowledge Management
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What is KM? KM in a company KM on Semantic Web Project: Ontoknowledge
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What is KM? Acquiring knowledge -Gather -Organize Maintaining knowledge -Represent -Update Accessing knowledge -Search -Visualize/browse -Share
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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
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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
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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
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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
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Manual Ontology Building and Instantiation OntoEdit -A tool for building an ontology and instances manually
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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
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Visualization Spectacle: ontology-based knowledge presentation
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Case Studies Swiss Life British Telecom
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Swiss Life IAS (International Accounting Standard) -Searching a large document on the Intranet OntoExtract -Learning ontology from documents -Assisting in reformulating user’s query
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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
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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
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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
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Application III Web Services
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Current web services Semantic Web services DAML-S Project: RETSINA calendar agent
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Toward Int’l Semantic Web Conference To attend ISWC 2003 in Florida…..
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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
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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
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A Framework DAML-S Adapted from IEEE Intelligent Systems
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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
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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
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Upper Ontology of Services Adapted from AAAI
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Upper Ontology of Services Adapted from AAAI
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Upper Ontology of Services Adapted from AAAI
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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
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(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
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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
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RETSINA Multi-agent system Developed by Katia Sycara et. al. (CMU) http://www.daml.ri.cmu.edu/site/projects/RDFCalendar/
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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
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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
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Services Beyond RETSINA Cooperation with other agents on Semantic web -Reminding upcoming registration or submission deadlines -Booking a flight to a conference
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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
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Summary Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management
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Summary Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management
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Summary Semantic WebData Mining Language Technology NLP/IE Agent Web Service Information Retrieval AI Machine Learning Knowledge Management
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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
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Q & A Thank you!
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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/
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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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