1. @ On Boosting Semantic Web Data Access Li Ding Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County Advisor: Tim Finin Date: Jan 19, 2005

2. @ 2 Outline Introduction  Thesis statement  Contributions to computer science Research description Research plan Preliminary and planned work  WOB-CORE: modeling the Semantic Web with its context  Swoogle: digesting and searching the Semantic Web  WOB: evaluating semantic web data quality Summary  Thesis schedule

3. @ 1. Introduction The Semantic Web in the Web Motivation Thesis statement

4. @ 4 The Web The Semantic Web in the Web Semantic Web Data Access wrapper service database (Web) document Static RDF document RDF/XML, N3, N-Triple, OWL/XML… RDF Graph Agent & Web Service HTTPHTTP, SOAPFIPA, SOAP,… Agent World Inference Translation Application

5. @ 5 The growing semantic web data More data ( from Swoogle Today, Jan 16, 2005 )  335,858 RDF documents (v.s. Google 8,058,044,651)  156,504 ontological terms (classes or properties)  46,987,876 triples Well populated ontology (organization adoption)  Blog, News feed (e.g. rss)  Personal homepage and social networking (e.g. foaf, bio)  Digital library (e.g. dc, dcTerms),  Copyright – creative commons (cc)  Software configuration (trustix)  Dictionary (e.g. wordnet)  Scientific data ( e.g. CRISISCat - California Invasive Species Information Catalog)CRISISCat Potential semantic web data  Bibliography  CIA world fact book

6. @ 6 Three challenges before utilizing semantic web data Semantic Web Where does George live ? Ontology dictionary Data access service Which `live’ ? Get it ! Quality of RDF graph Which to believe? Web scale semantic web vocabulary and data access source Joe Rank? Trust ? I mean ex:livesIn foo:George ex:livesIn ex:TheWhiteHouse foo:George ex:livesIn ex:Texas foo:George ex:livesIn ?x 1 2 3

7. @ 7 Motivation The utility of semantic web data access depends on three factors  Availability: how much semantic web data is available in the Web  Accessibility: how easily and effectively can users obtain the data they want  Quality: how well can semantic web data satisfy users’ requirements Applications  Spire: sharing scientific information using the Semantic Web  SemDis: discovering and evaluating semantic associations in the Semantic Web Utility SWDA = f (Availability, Accessibility, Quality)

8. @ 8 Spire is a distributed, interdisciplinary research project exploring the use of semantic web technologies in support science in general and the field of ecoinformatics in particular. Ecological Networks California Invasive Species Information Catalog UMBC Tree Survey NBII-CAIN Pacific Ecoinformatics and Computational Ecology Lab Darwin Core ebiquity@UMBC MindSwap@UMCP SF Tree Survey who@where How to search and use these data ? publisher creator Sharing semantic web data published by different sources throughout the Web http://spire.umbc.edu/

9. @ 9 Al-Qaeda Mr.X Terrorist Group isPresidentOf listedIn Company A invests Organization B Osama Bin Laden memberOf Afghanistan locatedIn Mr. Y ownedBy locatedIn relatedTo US locatedIn Kabul basedIn Afghanistan Company A Osama Bin Laden NASDAQ CIA World Fact Book CIA Agent W Department of State Organization B FOO News Kabul Agent K Discover complex semantic associations in SW. Evaluate trustworthiness of discovered associations Step1 Collect semantic web data from multiple sources and merge a big RDF graph. Step 2 Discover paths from Mr.X to Osama Bin Laden in the big RDF graph. Step 3 Evaluate trustworthiness of a discovered path with provenance and trust data http://semdis.umbc.edu/

10. @ 10 Research overview Semantic web vocabularySemantic web data access service Quality of RDF graph 2. Swoogle: Digesting and Searching the Semantic Web 1. WOB-CORE: Modeling the Semantic Web and its context 3. WOB: Evaluating semantic web data quality quality accessibility Consistency Importance Trustworthiness Identify dimensions Rank importance Evaluate trustworthiness Discover SW Digest SW metadata Search & navigation Search URIrefs Map URIrefs Search Ontologies Search RDF documents Semantic web “hyperlink” Utility Concepts Associations

11. @ 11 Thesis Statement Finding and evaluating information in the large scale Semantic Web is critical to users’ adoption but is not met yet. We developed Web of Belief (WOB) ontology, Swoogle data access service and data quality evaluation mechanisms to address these issues. These tools are proven to be effective in building semantic web metadata and boosting web- scale semantic web data access in applications like SemDis and Spire.

12. @ 12 Contributions to computer science WOB is the first ontology that captures and collects the metadata of the Semantic Web and its context  RDF graph reference language  Finer provenance model Swoogle is one of the first data access services that digest and search the web-scale semantic web.  Adaptive semantic web discovery agent  Semantic web metadata RDF graph abstract Ontology dictionary Recognized more relations among resources and document  Semantic web search and navigation model and service One of the first works that investigate semantic web data quality  Ranking the Semantic Web. We identified multiple navigation models for ranking.  Evaluate RDF graph's trustworthiness.

13. @ 2. Research Description Modeling the Semantic Web with its context Digesting and searching the Semantic Web Evaluating semantic web data quality

14. @ 14 Agent World The Semantic Web and its context The RDF Graph World The Web RDF Document serializes RDF graph Agent createsbelieves trusts RDF resource uses Ontology defines trust provenance subClassOf legends Person subClassOf Document subClassOf

15. @ 15 Modeling the Semantic Web and its context Goals  Identify concepts and associations  Build an ontology in OWL semantics, especially RDF graph reference language Finer provenance  Populate this ontology by rule-based translation Principles  Build simple, clear and minimal ontology  Reuse existing ontology  Show entity identity  Be aware of inference tractability Evaluation  Analytical comparison with other existing ontologies.  Satisfy applications (Swoogle, SemDis) requirements

16. @ 16 Related works WOB-core Ontology  Meta-ontologies: RDF, OWL  Popular ontologies: FOAF, DC RDF graph reference  Naïve approach: RDF test, OWL test  RDF reification: RDF specification  Named graphs (Carroll et al.2004) Provenance  Digital library (e.g. Dublin Core)  Database: data provenance (Buneman, Khanna, & Tan 2001) view maintenance (Cui, Widom, & Wiener 2000)  AI: knowledge provenance (da Silva, McGuinness, & McCool 2003; Fox & Huang 2003) proof tracing, PML (da Silva, McGuinness, & Fikes 2004); TELLIS(Gil & Ratnakar 2002)

17. @ 17 Web-scale semantic web data access model agent data access servicethe Web Discover RDF Docs ask (term) Compose query ask (query) inform (term URIrefs) Fetch docs Compose Local RDF graph Query local RDF graph Digest RDF Docs &Terms inform (doc URLs) Search Terms Search RDF Docs

18. @ 18 Digesting and searching the Semantic Web Goals:  Web-scale semantic web data access model  Data access service Adaptive RDF document discovery Digest semantic web metadata Semantic web search and navigation model and service Principles  Scalable design  Real world application Evaluation  Statistical report on collected metadata, web service usage  Precision and recall of search result  Users’ satisfaction on search and navigation model

19. @ 19 Related works: SW vs. Web IR vs. DB SW vs. Web IR: vocabulary, data model, query SW vs. DB: implicit data, query scale, vocabulary

20. @ 20 Related works (cont’d) Swoogle  Ontology based annotation systems Annotate web documents  SHOE (UMCP, 1997)  Ontobroker (AIFB, karlsruhe, 1998),  WebKB (Martin & Eklund, 1999),  QuizRDF (BT,2002) Annotate proper reference & relations  CREAM (AIFB,2003)  Ontology repositories DAML ontology library Schema Web Semantic web central  Semantic web ontology browsers W3C’s Ontaria (2004)  Semantic web instance databases Semantic web search Discovery  Meta-crawler  focused crawler  sw-crawler Digest  DC  W3C’s Annotea  OWL & RDFS Search & Navigation  Web IR (TFIDF)  RDF database query (e.g. RDQL, SPARQL)  Term navigation (e.g. Ontaria, Hyperdaml)

21. @ 21 Evaluating semantic web data quality Goals  Investigate dimensions of semantic web data quality  Evaluate semantic web data quality Ranking RDF resources and RDF documents Evaluating RDF graph trustworthiness  Trust and provenance based semantic web navigation model Principles  Semantic web data quality dimensions vary for different granularity and/or background knowledge Evaluation  Analytical analysis and proofs over navigation models and trust propagation models  Simulation (Ding et al. 2004b) for quantifying convergence & effectiveness  Application (Spire, SemDis) users’ feedback

22. @ 22 Related works Data quality dimensions  Information science (Wang, Storey, & Firth 1995) categorize data quality dimensions by domain interests Integrity (Database) User-satisfaction (Psychology) Statistics (auditing methods) Ontological world-modeling (Wand & Wang 1996)  Imperfect information Taxonomy : (Smithson 1989) (Smets 1991) (Parsons 1996) Computational models (Parsons & Hunter 1998)  probabilistic theory,  possibility theory,  evidence (Dempster-Shafer) theory.

23. @ 23 Related works (cont’d) Ranking  Complex network analysis (Newman 2003)  Text document ranking  Web page ranking: PageRank (Page et al. 1998; Haveliwala 1999), Hits(Kleinberg,1998)  Semantic ranking: Ranking RDF resources: (H.Zhuge & Zheng 2003) Ranking RDF document: Swoogle (contributed by Tim Finin, Rong Pan, 2004)  Social network analysis Trustworthiness  Content analysis: RDF graph difference (Berners-Lee & Connolly 2004).  Context analysis: semantic web trust layer Information security (Hyvonen 2002) Trust network (Golbeck, Parsia, & Hendler 2003; Richardson, Agrawal, & Domingos 2003; R.Guha et al. 2004; Ding,et al. 2004) semantic web publishing (Carroll & Bizer 2004). SWAD-Europe’s trust ontology (Arenas et al. 2004)

24. @ 3. Research Plan Research objectives and status

25. @ 25 Research objectives and status PhaseObjectivesArtifacts to produce 1WOBWOB-core ontology (w provenance) RDF graph reference language Provenance translated WOB-core instances 2Swoogleadaptive discovery agent semantic web metadata * search and navigation services* Swoogle statistics * 3SW data quality WOB-quality extension navigation and ranking model trust inference algorithms trust based navigation model 4FinalizeDissertation 1.Prototype 2. Complete & revise prototype 3. Evaluation & Justification Spiral research model * This is a joint work with others.

26. @ WOB-Core ontology RDF graph reference Provenance Status and next step Preliminary and planned Work: Web Of belief (WOB)

27. @ 27 Agent World WOB-core ontology The RDF Graph World The Web wob:RDFDocument wob:RDFgraphRef foaf:Agent rdfs:Resource owl:Ontology foaf:Person foaf:Document Association wob:source wob:Association wob:connective rdfs:domain rdfs:subClassOf wob:isdefinedby dc:source wob:creator wob:sourceDocument rdfs:subPropertyOf

28. @ 28 RDF graph reference Reference entire RDF graph  Reference the RDF graph from a document  Reference the RDF graph defined by usePattern Reference partial RDF graph  Accept a set of triples  Reject a set of triples  Special cases Referencing class instance Wildcard : “John hasChild _:x”

29. @ 29 RDF graph reference: an example wob:RDFGraphRef wob:RDFDocument http://foo.com/ex1.rdf wob:SimpleTriple foo:George ex:livesIn ex:Texas rdf:type wob:sourceDocument wob:usePattern wob:subject wob:predicate wob:object foo:George foaf:mbox ex:livesIn george@foo.com ex:Taxas http://foo.com/ex1.rdf

30. @ 30 Provenance in the Semantic Web WhereWhomwhyDefinition RDF Resourcedc:sourcedc:creatorrdfs:isDefinedBy RDF graph RDF documentdc:sourcedc:creator We differentiate the rdfs:range of provenance relation The scope of provenance property  Minimum semantic element: the semantic will not be complete when any triple is removed Complete: the entire sub-tree URI-complete: minimal sub-tree ends without blank nodes  dc:creator semantics Class instance Class/property definition Document

31. @ 31 Provenance of RDF graph Bob (said so) http://foo.com/example.owl “A is sub class of B” whomwhere why implies “A is sub class of C” “C is sub class of B” “Transitive rule” owl:Class ex:Aex:B rdfs:subClassOf rdf:type supports “x is instance of both A and B” whom

32. @ 32 Provenance of RDF resource and RDF document Bob (said so) http://foo.com/example.owl “A is sub class of B” foo.com Whom (dc:creator) where (dc:source) owl:Class ex:Aex:B rdfs:subClassOf rdf:type Definition (rdfs:isDefinedBy) Whom (dc:publisher) where (dc:source) Whom (dc:creator) why

33. @ 33 Proof WOB-provenance AgentRDF Document RDF Graph Website wob:sourceDocument TBD wob:creator dc:creator rdfs:subClassOf RDF Resource wob:sourceDocument wob:isDefinedBy rdfs:isDefinedBy dc:source wob:sourceDocument dc:source TBD wob:creator dc:creator wob:creator dc:creator

34. @ 34 Status and next step We have  Constructed WOB conceptualism  Proposed prelim RDF graph reference language  Classified provenance in the Semantic Web We will  Refine and evaluate WOB-core ontology  Complete RDF graph reference language  Add why-provenance  Populate WOB-core instances using rule based translation  Evaluate WOB-core ontology

35. @ Preliminary and planned Work: Swoogle Discovery Digest Search and navigation Status and next step

36. @ 36 The role of Swoogle in the Semantic Web Semantic Web Services Semantic web data Software Agents, Applications SW data service database (Web) document RDF document uses Directory/Digest Service Service Finder digests searches Data Finder SwoogleSwoogle

37. @ 37 Discovery - research Crawlers  Google-crawler  Focused-crawler  Semantic-Web-crawler, e.g. scutter RDF document word indicator  Keywords (positive list and negative list) filetype: 10 positive, over 100 negative url-pattern content-pattern  Google cat-words (to refine Google query) Revisiting URLs  The would-be RDF document  The out-of-date RDF document: changed, deleted  The redirected RDF document

38. @ 38 Discovery – current status Crawler performance  Google crawler is the best  Focused crawler needs to be improved 1/3 URLs are verified pure RDF documents Embedded RDF graph. RDF docsNon-RDF docsUndecidedTOTAL Focused Crawler1,4657%10,58052%8,29220,337 google crawler273,02336%369,37149%110,794753,188 SW_crawler61,87015%285,50670%57,709405,085 TOTAL336,358 665,457 176,7951,178,610 Source: Swoogle (2005-Jan-05) SELECT `discovered_by`, sum(isRDF), sum(1-isRDF), count(*) FROM `digest_url` WHERE 1 group by discovered_by

39. @ 39 Digest -- research RDF document annotation (join work) RDF graph abstract Ontological term definition Relations (join work)  Document-term relation  Document-document relation  Term-term relation

40. @ 40 RDF document annotation (join work) Document  filetype (suffix of URL)  When/how discovered  Last modified time  Document hash  Crawling info RDF/OWL level  RDF Syntax  SW language  OWL species  Provenance (creator, publisher) Ontology  Label  Version  Comment

41. @ 41 RDF graph abstract Possible models  Bag-of-word : literal, local name of resource  Bag-of-URI: URIrefs of non-blank RDF node  Triple: swangled triple digest (Mayfield & Finin 2003)  Ontological term: defined/referenced/populated class/property  Namespace: used/defined namespace  Identity: identity of class instance Possible methods  Document vector  Bloom filter (Bloom 1970)

42. @ 42 Ontological term definition foaf:name rdf:type owl:Class rdf:type “Person” rdfs:label foaf:name “Tim Finin” “Tim’s FOAF File” dc:title foaf:mbox rdfs:domain foaf:Agent rdfs:subClassOf Term Definition rdfs:subClassOf -- foaf:Agent rdfs:label – “Person” Empirical C-P bond foaf:name dc:title Ontological C-P bond foaf:mbox foaf:name rdfs:domain file1 file3 file2 foaf:Person

43. @ 43 Relations: doc-term; doc-doc; term-term rdfs:Resource wob:RDFDocument owl:Ontology rdfs:subClassOf swoogle:isUsedBy swoogle:sameNamespace swoogle:sameLocalname C-P bond, P-C bond any RDF triple swoogle:uses swoogle:defines wob:isDefinedBy swoogle:populatesClass swoogle:populatesProperty swoogle:refersClass swoogle:refersProperty swoogle:definesClass swoogle:definesProperty foaf:Document rdfs:seeAlso rdfs:isDefinedBy swoogle:officialOnto swoogle:extensionOnto owl:imports owl:priorVersion owl:backwardCompatibleWith owl:imcompatiableWith

44. @ 44 Search & Navigation -- research The Semantic Web is not simply the Web Search service  Document search – RDF document is not free text  Term search – URIref contains compound local name Navigation service  The RDF graph – Typed links  The web of RDF documents – Few hyperlinks  The social network of agents – trust & provenance

45. @ 45 URL URIref Semantic web search/navigation model Resource RDF Document usesdefines isDefinedBy officialOnto extensionOnto OntologyProperty rdfs:seeAlso rdfs:isDefinedBy Ontology isUsedBy rdfs:subClassOf sameNamespace sameLocalname ANY RDF PROPERTY Term Search Document Search 1 23 4 5 6 7 Keywords+ Filters SPARQL RDF graph

46. @ 46 Status and next step We have  Built a automatic semantic web discovery agent  Digested part of semantic web metadata RDF document annotation Relations: res-res; res-doc; doc-doc  Proposed semantic web search/navigation model with prototype implementation We will  Make the agent adaptive  Explore efficient RDF graph abstract  Provide a complete search/navigation service, esp. Swoogle search with SPARQL search support Ontology dictionary with user-friendly navigation interface  Complete Swoogle web service  Complete Swoogle statistics for quantitative evaluation

47. @ Preliminary and planned work: Semantic Web Data Quality Dimensions of semantic web data quality Ranking RDF resources and RDF documents Evaluate RDF graph trustworthiness Trust based navigation Status and next step

48. @ 48 Dimensions of semantic web data quality RDF graphRDF graph +RDFS/OWLSW metadata +trust weighted directed graph RDF graphSW + WebSW + Web + agents Term Importance centrality betweenness rel-vaguenss Importance RDF Document Importance RDF graph graph structure definition closeness semantic consistency rel-completeness credibility Agent credibility More to consider term correlation (C-P bond, P-C bond)

49. @ 49 Ranking RDF documents and RDF resources PageRank like navigation model  Background knowledge decides w(p) – how credits are distributed along semantic paths from one node Different context  RDF graph as weight directed graph  RDF graph + RDFS/OWL  RDF graph + RDFS/OWL + WOB (semantic web metadata)

50. @ 50 Navigation model 1: RDF graph RDF node Named edge Let wg(e) be the frequency of named edges in the given RDF graph Given a node p, each edge e from p is assigned with weight wg(e), and w(p) is the normalized vlaue

51. @ 51 Navigation model 2: RDF graph +RDFS/OWL Individual Class Meta Class Property type Literal / Resource type* Individual => Property is made by reading triple type* is valid in OWL-FULL semantics Literals and non-instance resources are ignored  Except owl:InverseFunctionalProperty is considered (OWL-FULL) InverseFunctionalProperty

52. @ 52 a2http://foo.com/ex.owl wob:sourceDocument wob:RDFDocument rdf:type foaf:Document rdfs:subClassOf rdfs:Class rdf:type rdfs:Property wob:source rdfs:label rdfs:range rdfs:subPropertyOf rdf:type dc:title An example

53. @ 53 Navigation model 3: RDF graph +RDFS/OWL+WOB Individual Class Meta Class Property type RDF Document Ontology We assume Swoogle search/navigation services is used. Rank RDF resources and RDF documents together

54. @ 54 Evaluating trustworthiness [Definition] A philosophical and context dependent concept. Common interpretations are reliance, faith, and confidence. Examples  “Is the triple (foo:George ex:livesIn foo:WhiteHouse) credible? ”  “Does foo:George (an instance of foaf:Person) always telling truth? ” Related terms  Belief: Trustworthiness of an RDF graph (by individual agent)  Trust: Trustworthiness of an agent’s beliefs (by individual agent) [KR] An agent’s belief (assertion) [ML] A hypothesis of the other agents’ belief quality [SNA] A context dependent inter-agent relation  Reputation: Social trustworthiness of an agent (by the public)

55. @ 55 How statement is justified trustworthy I’ve been to Foo many times, and the food was always good! I believe that “Restaurants with good outlook are good” “Foo has good outlook”; I believe that “Restaurants with good outlook are good” “Foo has good outlook”; My friends (who have similar taste as me ) said so. No better alternative inductive deductive conclusive (mimic) prima facie (at first view) Foo is a good restaurant I believe that “Good restaurants has good outlook” “Foo has good outlook”; I believe that “Good restaurants has good outlook” “Foo has good outlook”; abductive

56. @ 56 Trust propagation in justification Deductive – trustworthiness propagates from the premise w.r.t. inference rule  P -> Q, tv(Q) = tv(P) *tv(P->Q) Abductive – trustworthiness propagates from the consequence w.r.t. trustworthiness of reversing inference rule  P-> Q tv(P) = tv(Q) * f ( tv(P->Q) ) Bayes Inductive – trustworthiness is derived from past experiences  Argumentation – logic coherence  Knowledge similarity – statistic coherence Conclusive – trustworthiness propagates from the other agents through social trust relation  Trust(A,B) tv(S,A) = tv(trust(A,B)) * tv(S,B)  Recommendation prima facie – blind trust  Tv(S) = constant (normal reputation)  Largest take all

57. @ 57 Agents The given RDF Graph RDF graph (w ontology) Evaluate RDF graph trustworthiness S1 S2 Foaf:person rdf:type owl:Class S3 Foaf:person rdf:type rdfs:Class foaf:knows Foaf:Person rdfs:Classowl:Class rdfs:subClassOf (social network) JoeMike trusts believes disbelieves (Conflict belief) 1 2 3 4 Remove independent assumption by using more data

58. @ 58 Trust and provenance aware navigation Mechanism  Only pursue highly trusted  Shortest distance principle  Derive trustworthiness using weighted consensus  No delegation Complexity control  Search Branch – trust filter  Search Depth small world Initiator’s control b a d e g f c h initiator distance=0 distance=1 distance=2 domain-refer refer-refer

59. @ 59 Status and next step We have  Revealed some dimensions of semantic web data quality  Proposed some ranking mechanisms based on different navigation models and background knowledge  Proposed some trust evaluation mechanisms based on different background knowledge  Proposed a trust based navigation model We will  Consolidate semantic web data quality dimensions with more formal description  Evaluate, justify and improve ranking and trust evaluation mechanims

60. @ Summary [R] Thesis Statement [R] Contributions to computer science Research time table Planned milestones

61. @ 61 Thesis Statement Finding and evaluating information in the large scale Semantic Web is critical to users’ adoption but is not met yet. We developed Web of Belief (WOB) ontology, Swoogle data access service and data quality evaluation mechanisms to address these issues. These tools are proven to be effective in building semantic web metadata and boosting web- scale semantic web data access in applications like SemDis and Spire.

62. @ 62 Contributions to computer science WOB is the first ontology that captures and collects the metadata of the Semantic Web and its context  RDF graph reference language  Finer provenance model Swoogle is one of the first data access services that digest and search the web-scale semantic web.  Adaptive semantic web discovery agent  Semantic web metadata RDF graph abstract Ontology dictionary Recognized more relations among resources and document  Semantic web search and navigation model and service One of the first works that investigate semantic web data quality  Ranking the Semantic Web. We identified multiple navigation models for ranking.  Evaluate RDF graph's trustworthiness.

63. @ 63 A tentative research time table PhaseObjectivesArtifacts to produceStatus (%) Time (months) 1WOBWOB-core ontology600.53 RDF graph reference language301 Provenance500.5 translated WOB-core instances01 2Swoogleadaptive discovery agent5015 semantic web metadata *501 search and navigation services *302 Swoogle statistics *301 3SW QualityWOB-quality extension2016 navigation and ranking model402 trust inference algorithms502 trust based navigation model801 4FinalizeDissertation44 TOTAL18 * This is a joint work with others.

64. @ 64 Planned milestones WOB-core ontology  It covered all required meta-concepts in Spire and SemDis. Swoogle  It indexed all semantic web data needed by Spire and SemDis. We are expecting millions of RDF documents to be indexed.  It performed better than Google or other semantic web portals in searching ontologies and URIrefs throughout the Web. We are also looking forward to searching class-instance data. Semantic web data quality  RDF documents and RDF resources can be ranked reasonably using semantic web metadata in WOB. We are expecting users’ satisfaction about Swoogle search precision.  RDF graph trustworthiness can be evaluated reasonably by using trust and provenance information in WOB.

65. @ 65 Publications Refereed Publications Li Ding et al., "On Homeland Security and the Semantic Web: A Provenance and Trust Aware Inference Framework", InProceedings, Proceedings of the AAAI SPring Symposium on AI Technologies for Homeland Security, March 2005."On Homeland Security and the Semantic Web: A Provenance and Trust Aware Inference Framework" Li Ding et al., "How the Semantic Web is Being Used:An Analysis of FOAF", InProceedings, Proceedings of the 38th International Conference on System Sciences, January 2005."How the Semantic Web is Being Used:An Analysis of FOAF" Li Ding et al., "Analyzing Social Networks on the Semantic Web", Article, IEEE Intelligent Systems, January 2005."Analyzing Social Networks on the Semantic Web" Li Ding et al., "Swoogle: A Search and Metadata Engine for the Semantic Web", InProceedings, Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management, November 2004."Swoogle: A Search and Metadata Engine for the Semantic Web" Li Ding et al., "Modeling and Evaluating Trust Network Inference", InProceedings, Seventh International Workshop on Trust in Agent Societies at AAMAS 2004, July 2004."Modeling and Evaluating Trust Network Inference" Li Ding et al., "Trust Based Knowledge Outsourcing for Semantic Web Agents", InProceedings, Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, October 2003."Trust Based Knowledge Outsourcing for Semantic Web Agents" Youyong Zou et al., "Using Semantic web technology in Multi-Agent systems: a case study in the TAGA Trading agent environment", Article, Proceeding of the 5th International Conference on Electronic Commerce, September 2003."Using Semantic web technology in Multi-Agent systems: a case study in the TAGA Trading agent environment" Non-Refereed Publications Li Ding et al., "Weaving the Web of Belief into the Semantic Web", Misc, submitted to WWW2004, May 2004."Weaving the Web of Belief into the Semantic Web"

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67. @ 67 Selected references (cont’d) Kanh, B. K.; Strong, D. M.; and Wang, R. Y. 2002. Information quality benchmarks: Product and service performance. Communications of the ACM 45(4):184–192. Kleinberg, J. 1998. Authoritative sources in a hyperlinked environment. In Proceedings of ACM-SIAM Symposium on Discrete Algorithms. Mayfield, J., and Finin, T. 2003. Information retrieval on the semantic web: Integrating inference and retrieval. In Proceedings of the SIGIR 2003 Semantic Web Workshop. McDermott, D. 2001. Why rdf’s reification doesn’t work. http://lists.w3.org/Archives/Public/wwwrdf- logic/2001Apr/0066. McKnight, D. H., and Chervany, N. L. 1996. The meanings of trust. MISRC Working Paper Series. Newman, M. E. J. 2003. The structure and function of complex networks. SIAM Review 167–256. Page, L.; Brin, S.; Motwani, R.; and Winograd, T. 1998. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project. Parsons, S., and Hunter, A. 1998. A review of uncertainty handling formalisms. In Applications of Uncertainty Formalisms. Parsons, S. 1996. Current approaches to handling imperfect information in data and knowledge bases. Knowledge and Data Engineering 8(3). R.Guha; Kumar, R.; Raghavan, P.; and Tomkins, A. 2004. Propagation of trust and distrust. In Proceedings of the 1st Workshop on Friend of a Friend, Social Networking and the Semantic Web. Richardson, M.; Agrawal, R.; and Domingos, P. 2003.Trust management for the semantic web. In Proceedings of the Second International Semantic Web Conference. Smets, P. 1998. Probability, possibility, belief: Which and where. Quantified Representation of Uncertainty and Imprecision 1:1–24. Smithson, M. J., ed. 1989. Ignorance and Uncertainty: Emerging Paradigms. Springer Verlag. Wand, Y., and Wang, R. Y. 1996. Anchoring data quality dimensions in ontological foundations. Communications of the ACM 39(11):86–95. Wang, R.; Storey, V.; and Firth, C. 1995. A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering 7(4):623–639.

68. @ 68 Some ontologies and their QNames QNameNameURL rdfResource Description Frameworkhttp://www.w3.org/1999/02/22-rdf-syntax-ns# rdfsResource Description Framework schemahttp://www.w3.org/2000/01/rdf-schema# owlWeb Ontology Languagehttp://www.w3.org/2002/07/owl# rssRDF site summaryhttp://purl.org/rss/1.0/ foafFriend Of A Friendhttp://xmlns.com/foaf/0.1/ dcDublin Core Elementshttp://purl.org/dc/elements/1.1/ bioA vocabulary for biographical informationhttp://vocab.org/bio/0.1/ cccreative commonshttp://web.resource.org/cc/ trustix(used but not publicly defined)http://www.trustix.net/schema/rdf/spi-0.0.1# wordnetWordnet (Princeton U.)http://xmlns.com/wordnet/1.6/