Traversal and relations discovery among business entities and people using Semantic Web technologies and Trust Managemen

1. Dejan LavbičDejan.Lavbic@fri.uni-lj.si http://www.lavbic.net Traversal and relations discovery among business entities and people using Semantic Web technologies and Trust Management University of Ljubljana,Faculty of Computer and Information Science, SLOVENIA

2. Agenda • Motivation » semantic integration » problem of trust • Problem • Trust and semantic integration of data » modelling trust • SocioLeaks case study » technology » ontologies » example case study • Conclusions

3. Motivation (1) • semantic integration of various data sources that include information about business entities and people • the problem of trust as a method of dealing with uncertainty • especially when dealing with online personal identity • government registers vs. online social networks, newspaper archives etc.

4. Motivation(2) Identify person from keyword and display known properties. • Sources • Wikipedia • Freebase • …

5. Problem (1) • Lack of semantically integrated informationabout online personal identity with the purpose of: • coping with corruption in crossing the frontiers of legislation, • fraud detection in banks, insurance companies and other public institutions, • pattern discovery and identification.

6. Problem(2) • current approaches deal with integration of information from several data sources and omit or don't fully address the aspect of trust, • main focuson personal information from social networks which are not very reliable as users for various reasons tend to give false information.

7. Trust and semantic integration (1)Definition of trust • Trustis … • a measurable belief that utilizes personal experiences • experiences of others or possibly combined experiences, to make trustworthy decisionsabout an entity • a trustworthy decision is assumed to be a transitive process such that there is a web of trust network in which a link between two entities means that a trustworthy decision has been made and the quantitative value of that trust has been evaluated.

8. Trust and semantic integration(2)Modelling trust(1) • our approach is based on RDF language (extends to RDFS, OWL etc.), • different types of trust can be defined for each entity • data source trust • entity trust, which further consist of • schema level entity trust • instance level entity trust

9. Trust and semantic integration(3)Modelling trust(2) • trust of entity e » • entity trust » • schema level entity trust » • instance level entity trust » • degree of incorporation of users' votes »

10. Trust and semantic integration(4)Modelling trust(3) • trust of entity e » • entity trust » • schema level entity trust »

11. Trust and semantic integration (5)Modelling trust » example (4) • What degree of confidence does the information about the instance of a class Person represent?

12. SocioLeaks case study(1)Technology • Open source technologies that support current W3C standards in Semantic Web and linked-data applications • Apache Jena framework

13. SocioLeaks case study(2)Ontologies

14. SocioLeaks case study (3)Prototype example (1) Traversal is performed by specifying entry point of 1 or 2 entities. Defining the length of property paths to follow. The trust level threshold. Considering the time dimension. Filtering of entities and relations.

15. SocioLeaks case study (4)Prototype example (2)

16. Conclusion • Proposed the use of Semantic Web technologies for semantic integration of data about business entities and peoplecoupled with trust layer. • Several layers of trust – data source, schema level entity and instance level entity. • Enables filtering the data based on the user preference. • Application of the approach is feasible in several cases – banks, insurance companies etc.

17. Discussion • Thank you for your attention! • Questions, comments and critiques are more than welcome! » http://www.lavbic.net » Dejan.Lavbic@fri.uni-lj.si » @dlavbic