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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. Outline. Introduction Thesis Statement Research description Research plan

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web of belief modeling and using trust and provenance in the semantic web

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

outline
Outline
  • Introduction
    • Thesis Statement
  • Research description
  • Research plan
  • Preliminary Work
    • The Web Of Belief Framework
    • Evaluation
  • Contributions to computer science
  • Thesis Schedule
motivation
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
motivation cont d
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
motivation cont d5
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
thesis statement
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.

general description
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
the infrastructure of the semantic web
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

assumptions
Assumptions
  • Propositional knowledge (facts)
  • Uncertain knowledge with provenance
  • Open and distributed knowledge storage
relationship to other work
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)
logical formalisms
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
learning agent models
Learning Agent models
  • Objects to be learned
    • Domain Trust
    • Referral Trust
  • Methods
    • Histogram
    • Feedback based
reason over uncertain knowledge
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
two level data access
Two level data access
  • Datalog
  • Logical level
    • RDF data access language (with provenance)
      • Quads
      • TriQL
      • SPARQL
  • Storage level
    • Centralized
      • triplestore
      • Kowari
    • Decentralized
      • Search engine?
example walkthrough
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
relationship to other work18
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
approach the wob framework
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
research methodology
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
artifacts to be produced
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
limitations
Limitations
  • Limited in online Semantic Web documents
webofbelief ontology
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
slide26

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

data digest service
Data digest service
  • Support data access language
credibility assessment
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
application
Application
  • Trust based belief evaluation
  • Trust and provenance aware inference
  • Hypothesis testing and justification
evaluation
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
contributions
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
dissertation schedule
Dissertation schedule
  • Measures
    • Size of data that could be handle
    • Size of trust network
  • Milestones
    • Half-way
    • finished
slide36

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

slide37

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

expected contributions
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
1 web of belief represent the sw
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
ontology document namespace and term
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)

an abstract view of the semantic web
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

2 swoogle index service for sw
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
association feature
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

story 1 big rdf file p2p
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.
3 semdis how to judge information trustworthiness
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
practice of trust
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
story 2 identity
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
story3 knowledge fusion
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
story 4 justification markup language
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