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Modeling and Representing Trust Relations in Semantic Web-Driven Social Networks
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Modeling and Representing Trust Relations in Semantic Web-Driven Social Networks


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Master thesis defense: …

Master thesis defense:
"Modeling and Representing Trust Relations in Semantic Web-Driven Social Networks: An Ontological Analysis"

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  • I would like to thank you for giving me this pleasure to be at my defense presentation
  • Transcript

    • 1.  
    • 2. Modeling and Representing Trust Relations in Semantic Web-Driven Social Networks: An Ontological Analysis Nima dokoohaki Royal Institute of Technology, Stockholm, June 2007
    • 3. Agenda
      • Trust in Semantic web
      • Ontologies
      • Engineering of trust ontology
        • Development
        • Analysis
      • Conclusion
      • Future work
      • Questions
    • 4. Semantic web
      • An architectural plan to augment the existing web
      • Limitation of current web
        • Lack of Semantics for software agents
      • How :By Using a set of semantic standards
      • Goal : Giving the information on the web more meaning
      • Main Reason
        • More information accessibility and availability on the web
    • 5. Adopted from “ The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management ” – Daconta et al., 2003 Structure XML Schema Security and Identity Inference Engine DAML+OIL,OWL RDF/RDF Schema Reasoning and Proof Higher Semantics Semantics Trust Syntax Data XML Domain NameSpace Intelligent Domain Applications and Services
    • 6. Importance of trust in semantic web
      • Semantic web is an environment with certain features like;
        • Ubiquitous ,
        • Heterogeneous,
        • Openness .
      • In such an environment, trusting other people and their contributions becomes of great importance.
    • 7. Trust Definition
      • Tyron Grandison’s definition:
      • Trust is ” the firm belief in the competence of an entity to act dependably, securely, and reliably within a specified context ”.
      • Distrust is defined as the Lack of Trust.
    • 8. Trust properties
      • Trust is described to be: According to Grandison, and Abdul-Rahman et al.
      • Purpose or context-dependant . For instance, Alice trusts Bob as a doctor, but she might not trust Bob as a car mechanic.
      • Quantitative or qualitative (metric) . represents the intensity and level of trust. For instance, Alice might trust Bob as a doctor very much, while she only moderately trusts Martin as a doctor.
      • Transitive , or Non-transitive . when Alice trusts Bob and Bob trusts Anna, Alice will trust Anna.
      • Dynamic . Trust is dynamic and it is permanently changing.
      • Non-monotonic . Further observations may elevate or lower the level of trust invested in another entity.
    • 9. Trust properties contd.
      • Two distinctions are made;
      • Distinction 1:
        • trust in performance : trust in an entity to perform an action,
          • Alice trusting Bob as a doctor
        • trust in recommendation : trust in an entity to recommend other entities to perform that action.
          • Alice trusting Bob to recommend a good doctor
      • Distinction 2 : Considering existence of recommenders
        • trust from direct observation of the trustee
        • trust derived from the observations by the recommenders
    • 10. Social Networks
      • WBSN, Web-based social networks
      • Evolved beyond text messages
      • Ability to specify and state reliability, trust and belief
      • Survey done by Jen Golbeck done in 2005 : “ 115,000,000 members of the social networks discovered across about 18 networking societies .“
        • Another big bang is quite close!
      • Solution : integration and merging of the data distributed across these networks
    • 11. Semantic Social Networks
      • SSN , describes the notion of integrated and merged social networks . [Stephen Downes, 2004]:
        • “ Network resources are expressed in XML or RDF , such as descriptions of persons (authors, readers, critics)
        • References to those descriptions employed in RDF or XML files describing resources.”
      • potential solution for merging and integration of information
        • The Friend-of-a-Friend ( FOAF ) Project [Dumbill, 2002]
    • 12. Foaf: A framework ,a vocabulary and an ontology
      • FOAF is “ a framework for representing information about people and their social connections ” . Golbeck’s definition
      • The FOAF Vocabulary [Brickley, Miller, 2004] contains terms for describing and depicting personal information (such as name, surname and email address), membership in groups and social connections
      • FOAF is represented as an ontology , using RDF and OWL
    • 13. Sample Foaf profile
      • <foaf: Person rdf:ID=&quot;me&quot;>
      • <foaf:name>Nima Dokoohaki</foaf:name>
      • <foaf:title>Mr</foaf:title>
      • <foaf:givenname>Nima</foaf:givenname>
      • <foaf:family_name>Dokoohaki</foaf:family_name>
      • <foaf:depiction rdf:resource=
      • &quot;;/>
      • <foaf:workplaceHomepage rdf:resource=&quot;;/>
      • <foaf:schoolHomepage rdf:resource=&quot;;/>
      • <foaf: knows >
      • <foaf:Person>
      • <foaf:name>Victor Duran Levin</foaf:name>
      • </foaf:Person>
      • </foaf: knows >
      • </foaf: Person >
    • 14. Ontology definition
      • a structure capturing semantic knowledge about a certain domain by describing relevant concepts and relations between them
      • a graph / network structure consisting from:
        • A set of concepts (vertices in a graph);
        • A set of relationships connecting concepts (directed edges in a graph);
        • A set of instances assigned to a particular concepts
    • 15. Semantic Web Ontology languages
      • RDF : the Resource Description Framework (RDF) and RDF Schema
        • is essentially a data modeling language
        • RDF is graph -based, but usually serialized as XML .
        • it consists of triples: subject , predicate , object .
      • DAML+OIL : Defense Advanced Research Projects Agency (DARPA) Agent Markup Language (DAML) + Ontology Inference Layer (OIL)
        • Fairly comparable to OWL
      • OWL : Web Ontology Language (OWL)
        • the most expressive of the ontology languages
        • provides mechanisms for creating all the components of an ontology
    • 16. Ontology construction
      • Defining domain and Scope
        • Semantic social networks, trust social networks on semantic web
      • Data understanding
        • Social meta-data; user profiles and information of people and their social connections
      • Task definition
        • Describing and representing trust relationships
      • Ontology learning
      • Ontology evaluation
      • Refinement with human in the loop
    • 17. Main sphere of trust ontology . 3 main Concepts of trust ontology as well as two relationships connecting them together. (visualized using Ontosphere3D plug-in for Protégé .)
    • 18. Relationship concept ; two main properties of hasTrustee And hasTruster defined on the range of Foaf-agent and connected to AuxiliaryProperties And MainProperties using two relations of hasAuxiliaryProperties and hasMainProperties
    • 19. MainProperties concept; having two main data type properties of trust subject (topical trust) , and trust value
    • 20. AuxiliaryProperties concept; having 3 data type properties of DateBegin , DateEnd and ContextType and also hasRecommender which is defined on the range of foaf agent
    • 21. Modeling trust
      • We have considered modeling trust
      • We have described trust in performance . When we state that “ Alice trusts Bob regarding Driving ”.
        • meaning that, “Alice trusts in eventuality of performance of Bob to some extent, when the act of driving is performed”.
      • Trust Metric: We have used a probabilistic approach to describe trust relationships, so we can say how much someone trusts the other on a range between 0 and 1.
    • 22. Modeling distrust and feelings
      • Distrust , is modeled implicitly .
        • For instance,” Alice distrusts Bob regarding babysitting to some extent (0.65)”, can be also stated like “Alice trusts Bob regarding babysitting to some (complementary) extent (0.35 )”.
      • Feelings is modeled to some extent.
        • Averaging all evaluation values for a relation , We can derive negative or positive feelings .
          • If Alice has low trust values for Bob, then we can state that she has negative feelings for him, or vice versa.
        • Limitation : there are many certain properties that should be considered
    • 23. Presentation of Network using our ontology
      • <foaf:Person rdf:ID=&quot; Alice &quot;/>
      • <foaf:Person rdf:ID=&quot; Bob &quot;/>
      • <Relationship rdf:ID=&quot;Relationship_Alice_Bob&quot;>
      • <hasTrustee rdf:resource=&quot;#Bob&quot;/>
      • <hasTruster rdf:resource=&quot;#Alice&quot;/>
      • <hasMainProperties>
      • <MainProperties rdf:ID=&quot;MainProperties_Alice_Bob&quot;>
      • <Subject rdf:datatype=&quot;&xsd;string&quot;> Driving </Subject>
      • <Value rdf:datatype=&quot;&xsd;float&quot;> 0.95 </Value>
      • </MainProperties>
      • </hasMainProperties>
      • <hasAuxiliaryProperties>
      • <AuxiliaryProperties rdf:ID=&quot;AuxiliaryProperties_Alice_Bob&quot;>
      • <ContextType rdf:datatype=&quot;&xsd;string&quot;>
      • Social Network
      • </ContextType>
      • </AuxiliaryProperties>
      • </hasAuxiliaryProperties>
      • </Relationship>
      Bob Alice Trust / Driving 0.95
    • 24. Trust networks of small size Increasing the length of the network structure, Network contains 20 nodes and 34 edges) Increasing the width of the network structure, (Network contains 28 nodes and 54 edges) Clara Bob David Alice Trust / Cooking Distrust / Dishwashing Trust / Driving Distrust / Teaching 0.76 0.46 0.96 0.80 Clara Bob David Alice
    • 25. Trust networks of larger size Hybrid Networks - Connected networks of different contexts ; a social and a business network. Example Hybrid networks, contain 8 people and 12 relations. 8 links are interconnections (local), and 4 links are acting as glue connecting two networks (foreign). Network contains 48 nodes and 92 edges . Clara Bob David Alice Ginger Eric Henrik Frida Social Network Business Network
    • 26. Trust networks of larger size Meshed networks –Motivated from real-world network formations, complex, combined networks of different sizes and different contexts. Partial or fully connected meshed networks. In sample, A partial meshed network made-up of two connected hybrid networks; This network contains 16 people and 26 relations. (Network contains 98 nodes and 198 edges) C B D A aB G E H F O M P N K I L J
    • 27. Structural comparison; Small sized networks Increase in length ; Networks of 4 people and 4 relationships. Increasing is width ; Networks of 4 people and 6 relationships.
    • 28. Structural comparison; large sized networks Hybrid Network ; Network of 8 people and 12 relationships. Meshed network ; Networks of 16 people and 26 relationships.
    • 29. Discussion on comparisons
      • In networks of small size, ontology shows average performance.
        • As the size of the networks increase, certain aspect of trust network size increases
      • Main reason, the number of elements incorporated within the structure of ontology.
        • Golbeck’s ontology uses only one main element, Konfidi uses two main elements, while our ontology uses three main concepts
      • Efficiency in design of the ontology
        • Efficient design has certain aspects that reduce the size of the networks generated using ontology ( Structural Determinism )
          • Management of over all organization
    • 30. Discussion on comparisons
      • Third reason is the AuxiliaryProperties
        • incorporating an extensibility element , incorporates extra edges and especially extra nodes into the network.
      • Important : None of the other compared ontologies, have no elements for describing extra properties ;
        • extending Golbeck’s trust ontology is very hard and needs a thorough change
        • Konfidi doesn’t have any elements for describing extra properties.
    • 31. Conclusion
      • We analyzed the modeling and representation of trust within semantic web-driven networking societies .
        • We used Ontologies as our modeling tool.
      • There are certain new features that our work introduces to trust ontologies in this context;
        • using our AuxiliaryProperties , we give relationships more weight and meaning .
        • We have introduced the hasRecommender property that can determine the strength of the links on social network
    • 32. Conclusion
      • As a conclusion, ontologies are very promising technologies.
      • Utilizing ontologies in modeling and representing trust in semantic web-enabled social networks seems to be a highly efficient methodology and mechanism
      • Maybe, “ KEY FREE TRUST” AT LAST !
    • 33. Future work
      • Spotting more research fields on overlapping areas between Social sciences and Semantic web
      • One of the most important future works is spotting further applications for social trust, where trust relationships can be modeled and expressed using ontologies.
        • Current applications are just limited to Spam filtering and user rating systems across web sites on internet.
    • 34. Questions
      • Thank you for your attention !
      • Nima Dokoohaki