Web of Belief: Modeling and using Trust and Provenance in the Semantic Web

1. Web of Belief: Modeling and using Trust and Provenance in the Semantic Web Department of Computer Science and Electronic Engineering University of Maryland Baltimore County Li Ding Last updated:3/10/2014

2. Outline • Introduction • Thesis Statement • Research description • Research plan • Preliminary Work • The Web Of Belief Framework • Evaluation • Contributions to computer science • Thesis Schedule

3. Motivation • The growing body of the Semantic Web • Observations • Information • More Data encoded in Semantic Web language from many sources • Various dialect Ontologies • Information is managed in two layer mechanism in terms of “Document, Ontology, namespace, term” • Physical layer: the web of semantic web documents • Logical layer: the RDF graph • More Semantic Web Tools • Drive forces • Industrial: Weblog, RSS, social network websites • Academic: research projects

4. Motivation (cont’d) • The Semantic Web has not achieved a real world “KB” • Credibility & Consistency • Facts are provided by many sources w/o guarantee • Scalability • Data is in vast amount • Data is stored in an open and distributed context • Utility • Data is fragmented • Bad URI Reference of resource & namespace in the Web of documents • Lack of associations in the RDF graph

5. Motivation (cont’d) • Why provenance and trust • Important concepts borrowed from human world • Multi-discipline origins: social, epistemology, psychology • The foundation of knowledge management and inference • Keys to credibility assessment and justification • Empirical heuristics, also the complement method, in the absence of domain knowledge to direct reason over credibility. • Explicit representation of justification trace. • Good Heuristics to resolve inconsistency. • Keys to effectiveness and efficiency • Knowledge can be managed by Provenance besides Topic • Trust reduces search complexity

6. Thesis Statement This dissertation shows that our Web Of Belief framework, a provenance and trust aware inference framework, is critical and effective in deriving answers with credibility assessment and justification across the open, distributed, and large scale online knowledge base provided by the Semantic Web.

7. Research Description

8. General Description • Goal: model and use provenance and trust in the SW • to enable a credible “world KB”. • to enable trust layer in the Semantic Web • Representation • Encode provenance and trust • Represent SW as KB • Inference • Hypothesis Test • Trust network computation • Statement credibility • Justification • Ontology Dictionary • Term definition • Class tree • Management • acquisition & digest • data access interface • Inference space expansion

9. The Infrastructure of the Semantic Web Applications uses uses Reputation Service Web entity directory searches Directory/Digest Service SW Service finder SW Data finder digests digests Computing Services Data Service RDF document SW data service database (Web) document

10. Assumptions • Propositional knowledge (facts) • Uncertain knowledge with provenance • Open and distributed knowledge storage

11. Relationship to Other Work • Representation • Logical formalisms of agent model (AI) • Truth theory (Epistemology) • Provenance • Data access • Collaborative KB in open distributed context (DB) • Learning • Learning agent models: knowledge and behavior (social learning & psychology) • Inference • Reason over uncertain knowledge (reasoning)

12. Logical Formalisms • Modal Logic -- logically formalize agent • Agent & action (McCarthy,1969; Kanger-Porn-Lindahl) • Agent & belief and intention (Cohen, Levesque,1990) • Agent & knowledge (Epistemic logic) • Agent & belief (Doxastic logic) • Agent & obligation (Deontic logic ) • Other logical formalisms for trust and belief • Regan’s formal framework for belief and trust • Josang’s subjective logic • Abdul-Rahman’s social trust model • Jones and Firozabadi’s integrated logic model of trust

13. Epistemology

14. Learning Agent models • Objects to be learned • Domain Trust • Referral Trust • Methods • Histogram • Feedback based

15. Reason over uncertain knowledge • Quantitative approach • Certainty factors - Mycin (Shortliffe, 1976) • (obsolete heuristic), similar to Fuzzy approach • Possibility theory: Fuzzy logic (Zade, 1965;1976) • Dempter-Shafer theory (Dempster,1968; Shafer 1976) • Subjective logic • Probabilistic theory: Bayes Network (Pearl;1982) • Qualitative approach • Non-monotonic logic

16. Two level data access • Datalog • Logical level • RDF data access language (with provenance) • Quads • TriQL • SPARQL • Storage level • Centralized • triplestore • Kowari • Decentralized • Search engine?

17. Example walkthrough • Given a hypothesis/query in form of a collection of RDF statements with or w/o variables • Provenance • where can I find them? • where are the definitions for each term? • Belief( agent, fact): Who said or asserted so? • Justify( fact, fact): • Trust • Can I believe them and thus use them in decision making • How do I trust the other agents

18. Representation Agent, knowledge Provenance Trust Data access Metadata RDF query language Pattern extraction Transitive closure RDF storage Inference Trust network inference Credibility Probabilistic inference Scalability Domain filter Social filter Semantic Web Relationship to Other Work

19. Research Plan

20. Approach – the WOB framework • Representation • WOB ontology • Model provenance and trust into the semantic web • Explicit represent the semantic web • Represent SW as a KB in terms of “agent, statement, association” • Management • Provenance aware data access language • Social network extraction and integration • Provenance and trust based knowledge base expansion • Inference • Hypothesis credibility assessment • Trust network inference • Provenance and trust based belief evaluation • Explicit justification • Ontology dictionary

21. Research Methodology • Identify real world problems with examples • Approach problems • Formalize problem • Position problem in literature, and find related work • Find issues to be resolved • Design and implement solutions • Evaluation methods • Statistics • Project application • Survey

22. Artifacts to be produced • [Data] Web Of Belief Ontology • [System] Swoogle metadata and search service • [System] Ontology dictionary • [Data] Swoogle Statistics • [System] SemDis Trust layer • [Algorithm] Trust based belief evaluation • [Algorithm] Trust based knowledge expansion

23. Limitations • Limited in online Semantic Web documents

24. Preliminary Work

25. WebOfBelief Ontology • Ontology • Entity: Document, Statement, Reference, Agent, • Association • Sub-classes: trust, belief, justification, dependency • Facets • Confidence (conditional probability) • Connective (semantics) • Provenance • (Agent-document) Ownership/Authorship • (Agent-Reference) belief • (Reference-Reference) justification • (doc-doc) dependency • Logical Formalisms

26. Web Of Belief (WOB) Conceptual Framework (v0.92) AssociationConnective xsd:real [0,1] confidence connective Association Justification Dependency Belief Trust foaf:Document Reference foaf:Agent selects foaf:page dc:creator contains rdf:Resource rdf:Statement source wob:support wob:weaken wob:cause wob:imply wob:truthful wob:wise wob:knowledgeable wob:cooperative wob:believe wob:disbelieve wob:nonbelieve wob:imports wob:priorVersion

27. Data digest service • Support data access language

28. Credibility Assessment • Trust Network Inference Given a trust network, how to propagate trust so as to evaluate trust between any two agents • Trust and provenance based statement evaluation • Explicit Justification

29. Ontology dictionary?

30. Social network extraction and mapping

31. Application • Trust based belief evaluation • Trust and provenance aware inference • Hypothesis testing and justification

32. Evaluation • Validate derived trust relations: survey users • Validate performance of WOB inference • Compare results w or w/o trust & provenance • Validate application utility: customer report

33. Contributions • A practical framework that makes the Semantic Web a KB • The Web of Belief Ontology • Semantic Web data digest service • Search and browse mechanisms for SW • Support of RDF data access language? • Inference • Judge information trustworthiness • The first work in characterizing the Semantic Web • trust and provenance aware distributed inference

34. Dissertation schedule • Measures • Size of data that could be handle • Size of trust network • Milestones • Half-way • finished

36. Trust Semantic Web P2P Possibility Theory Belief Theory the Semantic Web • Representation • Belief, trust • Policy, rule SW services SW intelligent user Reputation service • Inference • Derive trust • Belief fusion • Justification Inference Service SW service finder SW digest SW data finder Heuristic search Flexible query SW user SW digest Digest/Search Service SW data service Information protection SW file SW Composer compose Rich Information Text An outline of the Semantic Web

37. An example inference Sorry I don’t have it, Do you want US population? Find Washington Population disambiguation Which `Washington’ do you mean? Associations Belief. Who knows what? RDF reference How to refer part of RDF graph SW digest • Trusting provenance • Credential based trust • Reputation based trust • Context/Role based trust • Trusting content • consensus • context axioms Sure! the following SWDs/Agents know that Trust network discovery Uncertainty and Precision Trust network Here are the certainty/trustworthiness for each unique answer Justification Rule represent hypothesis Justification instantiates rule Oh Yeah! Answer X is credible because it comes from government website Fill a RDF template Show me the complete definition of class X

38. Expected Contributions • Framework • Features for characterize the Semantic Web • An Web of Belief ontology to connect the Semantic Web • Association/ annotation • Query language or data access language? • Mechanisms • Search/browse Semantic Web Document • Judge information trustworthiness • Applications • Swoogle • Semdis

39. 1. Web of Belief – represent the SW • Build an abstract view of the Semantic Web • Select features to characterize it • Overall features: timeline, category • Different levels: term, document, network • Different classes: Entity, Association • Different semantics: Meta-ontology, domain-ontology • Build web of belief ontology for explicit representation

40. Ontology, Document, Namespace, and Term Namespace Local name uses (n:1) hasName (n:1) Term defines (m:n) contains (m:n) Document defines (m:n) Ontology SWDB Swoogle Search & Browse (1/3)

41. sameLocalName

42. An abstract view of the Semantic Web Network level Semantic Web Document level Document doc-doc association RDF Database RDF Node level RDF Node Node-node association node-doc association

43. 2. Swoogle – index service for SW • Even we have knowledge online, a portal data digest service is need to facilitate data access • RDF digest • Meta level (use RDF/OWL semantics) • Domain level (use domain semantics) • RDF query • Document • Term • Literal (name, identifier) • Dictionaries • Term/Ontology dictionary • Web entity dictionary

44. Association Feature Ontological annotation Empirical c-p definition rdf:type MetaC rdf:type Ontological c-p definition C P1 • node-node • Term-definition • class-property • Ontological • Empirical • meta association, e.g. rdfs:subClassOf, rdfs:domain • node-doc • resource, doc, #subject,#property,#object, #subject-type-X, #X-type-object • Literal, doc, predicate • doc-doc • Meta association, e.g. owl:imports • Namespace co-occurrence o1 I P2 --- rdf:domain rdf:range P3

45. Story 1: Big RDF file & P2P • Facts • We found WordNet has published its ontology in a 60M daml file, where JENA fails to load it in memory. • Most people use ontology as data exporting annotation, (Stefen Decker argues in WWW2004 Dev day), • Querying RDF should be tractable (Ian Harrock, Andy Seanbome). i.e. we need to balance the tractability and the expressiveness of a query. • the query result for a graph pattern (with variables) can be of three types: a subgraph, the variable binding, a max subgraph • Provenance information mainly range in Agent (person, organization, website). i.e. agent’s belief • Question • Is it appropriate to say a RDF model is a RDF file? If not, how do we describe a distributed RDF model? • Will there be any very big RDF file? Why? • Can we let RDF stored in small files and distributed throughout the world.

46. 3. SemDis: How to judge information trustworthiness? • Granularity • rdf:Statement • SWD • Information source (agent, website) • Topic • Association • Social network (FOAF) • Belief, Authorship (foaf:maker) • Justification • Trust computation • Ranking • Network Consensus

47. Fields Weblog FOAF RSS Online Social network DBLP FOAF Google Applications Manipulate precision Disambiguation: specialize knowledge Privacy protection: generalize knowledge Manipulate completeness – fuse knowledge Algorithms Trust propagation algorithm: surfer model, flow model, Belief merging algorithm Given A new statement Reasoning: What is its trustworthiness given opinions on it from some information sources? (subjective logic, fuzzy cognitive map) Justification: How to find evidences to support/weaken it? (web of belief ontology for annotation) Given A question Search: effective/efficient in open environment (rdf digest, bounded search with trust heuristic) Given Online multi-network Social relations among information sources (FOAF) Ontological relations among topics (sub-topic) Web entity identification and mapping Emergence model How these can really affect the semantic web research? Practice of Trust

48. Story 2: Identity • Facts • We found a lot social network online, e.g. coauthor(dblp), knows(foaf), colleague. Different networks adopt different identities • Each of them might not well connected, or quite small, but what-if we connected them • One identity shared by multiple persons, by mistake or by nature • Identity mapping is m:n • Questions • Can we determine certainty of identity • How to map identity

49. Story3: Knowledge Fusion • Fact • We can fuse person info. From multiple FOAF file. Some statements are confirmed by a lot of people • We can build a model which has multiple provenance • Questions • How to use provenance information to assure the receiver. • What if Dr. Joshi want to determine his trust to the ontology created by Dr. Amit Sheth

50. Story 4: Justification Markup Language • Facts about distributed justification on the web (semantic web) • The justification on the web may not always be formalized. • Knowledge on the web could be objective (like database) or subjective (like joke, estimation). • Knowledge on the semantic web is inherently inconsistent • Determining what counts as adequate reasons is an obstacle to providing justification. This process of reason giving can be viewed as argumentation in four major forms: inductive, deductive, conclusive, and prima facie. • Inductive and deductive justification involve evidence and logical evaluation. • In a conclusive argument, reasons are analyzed by asking if another rational human would have the same belief given the same reasons. • prima facie argumentation is a process of giving several reasons for believing something and choosing the most important one. • Question: • How to represent the mixture of human inference, statistical information and logical inference • Distributed justification: trust-based, case-based, logical-inference • Example: I will buy a new Honda Accord because • (1) [inductive] it is a good car because 90% related online comments are positive ; • (2) [deductive] it has better mile/gas performance; • (3) [conclusive/mimic] I will buy a car since my friend (who has similar taste as me ) like to buy it . • (4) [prima facie] Among all factors that make me happy, buying a new car is the most important • Solution • Formal language to express logical programming proof trace, e.g. PML • We also need informative language to express human justification • Express relation between statements: support, casual, critique, • Log decision process as a case for future sharing/recall/query. • Cite a case/used reason as proof of new justification