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

Slide 2: Modeling and Representing Trust Relations in Semantic Web- Driven Social Networks: An Ontological Analysis Nima dokoohaki Royal Institute of Technology, Stockholm, June 2007

Slide 3: Agenda • Trust in Semantic web • Ontologies • Engineering of trust ontology – Development – Analysis • Conclusion • Future work • Questions

Slide 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

Slide 5: Intelligent Domain Applications and Services Trust Security and Identity Reasoning and Proof Inference Engine Higher Semantics DAML+OIL,OWL Semantics RDF/RDF Schema Structure XML Schema Syntax Data XML Domain NameSpace Adopted from “The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management” – Daconta et al., 2003

Slide 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.

Slide 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.

Slide 8: Trust properties • Trust is described to be: According to Grandison, and Abdul-Rahman et al. 3. Purpose or context-dependant. For instance, Alice trusts Bob as a doctor, but she might not trust Bob as a car mechanic. 4. 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. 5. Transitive, or Non-transitive . when Alice trusts Bob and Bob trusts Anna, Alice will trust A nna. 6. Dynamic. Trust is dynamic and it is permanently changing. 7. Non-monotonic. Further observations may elevate or lower the level of trust invested in another entity.

Slide 9: Trust properties contd. • Two distinctions are made; • Distinction 1: 1. trust in performance: trust in an entity to perform an action, • Alice trusting Bob as a doctor 2. 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 1. trust from direct observation of the trustee 2. trust derived from the observations by the recommenders

Slide 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

Slide 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]

Slide 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

Slide 13: Sample Foaf profile <foaf:Person rdf:ID="me"> <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= "http://nimadokoohaki.googlepages.com/nimatbana.JPG"/> <foaf:workplaceHomepage rdf:resource="http://nimadokoohaki.googlepages.com"/> <foaf:schoolHomepage rdf:resource="http://www.imit.kth.se"/> <foaf:knows> <foaf:Person> <foaf:name>Victor Duran Levin</foaf:name> </foaf:Person> </foaf:knows> </foaf:Person>

Slide 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

Slide 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

Slide 16: Ontology construction 1. Defining domain and Scope – Semantic social networks, trust social networks on semantic web 2. Data understanding – Social meta-data; user profiles and information of people and their social connections 3. Task definition – Describing and representing trust relationships 4. Ontology learning 5. Ontology evaluation 6. Refinement with human in the loop

Slide 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.) Protégé

Slide 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

Slide 19: MainProperties concept; having two main data type properties of trust subject (topical trust), and trust value

Slide 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

Slide 21: Modeling trust • We have considered modeling trust • We have described trust in performance. When we state that “Alice trusts Bob regarding Driving”. 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.

Slide 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

Slide 23: Presentation of Network using our ontology Trust / Driving <foaf:Person rdf:ID="Alice"/> 0.95 <foaf:Person rdf:ID="Bob"/> Alice Bob <Relationship rdf:ID="Relationship_Alice_Bob"> <hasTrustee rdf:resource="#Bob"/> <hasTruster rdf:resource="#Alice"/> <hasMainProperties> <MainProperties rdf:ID="MainProperties_Alice_Bob"> <Subject rdf:datatype="&xsd;string">Driving</Subject> <Value rdf:datatype="&xsd;float">0.95</Value> </MainProperties> </hasMainProperties> <hasAuxiliaryProperties> <AuxiliaryProperties rdf:ID="AuxiliaryProperties_Alice_Bob"> <ContextType rdf:datatype="&xsd;string"> Social Network </ContextType> </AuxiliaryProperties> </hasAuxiliaryProperties> </Relationship>

Slide 24: Trust networks of small size Increasing the length of the network structure, Network contains 20 nodes and 34 edges) Trust / Cooking Alice 0.76 Distrust / 0.46 Dishwashing Bob 0.96 Clara Trust / Driving Distrust / 0.80 Teaching David Increasing the width of the network structure, (Network contains 28 nodes and 54 edges) Alice Bob Clara David

Slide 25: Trust networks of larger size Alice Social Network Bob Clara David Frida Business Network Eric Ginger Henrik 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.

Slide 26: Trust networks of larger size A J a B B C I K D L N F M O E G P H 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)

Slide 27: Structural comparison; Small sized networks 40 35 30 25 20 Nodes 15 Edges 10 5 Edges 0 Golbeck Nodes Ours Konfidi Increase in length; Networks of 4 people and 4 relationships. 60 50 40 30 Nodes 20 Edges 10 Edges 0 Golbeck Nodes Ours Konfidi Increasing is width; Networks of 4 people and 6 relationships.

Slide 28: Structural comparison; large sized networks 120 100 80 60 Nodes 40 Edges 20 Edges 0 Golbeck Nodes Ours Konfidi Hybrid Network ; Network of 8 people and 12 relationships. 250 200 150 Nodes 100 Edges 50 Edges 0 Golbeck Nodes Nima Konfidi Meshed network; Networks of 16 people and 26 relationships.

Slide 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

Slide 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.

Slide 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

Slide 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 !

Slide 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.

Slide 34: Questions • Thank you for your attention ! Nima Dokoohaki