An RDF Approach for Discovering the Relevant Semantic Associations in a Social Network

A social network is a network of interactions between entities of social interest like people, organisations, hobbies and transactions. Finding relevant associations between entities in a social network is of great value in many areas like friendship networks, biology and countering terrorism. Semantic Web technology enables us to capture and process relationships among social entities as metadata. Analysing semantic social networks requires newer methods. In a social network, entities are connected by short chains of relationships. Query to find associations between two entities returns a large number of results. One of the major issues is to rank the associations as per user preference. The work presents an approach to rank two categories of semantic associations viz. common associations and informative associations. Associations are modelled as property sequences in an RDF graph and they are ranked based on preferred search mode. Heuristics such as i) information content due to occurrence of a property with respect to all the properties in a description base ii) unpredictability of an association due to participation of its properties in multiple domains iii) the extent of match between user specified keywords and properties and iv) the popularity of nodes involved in a sequence are used to rank associations. The results obtained suggest that these heuristics indeed help in obtaining relevant associations. To scale the results to large RDF graphs, a relevant subgraph is extracted from the input graph on which ranking is applied. The approach is tested successfully on real RDF datasets and multigraphs.