A Model for Ranking Semantic Associations in a Social Network

Sunju Oh


Much Interest has focused on social network services such as Facebook and Twitter. Previous research conducted on social network often emphasized the architecture of the social network that is the existence of path between any objects on network and the centrality of the object in the network. However, studies on the semantic association in the network are rare. Studies on searching semantic associations between entities are necessary for future business enhancements. In this research, the ontology based social network analysis is performed. A new method to search and rank relation sequences that consist of several relations between entities is proposed. In addition, several heuristics to measure the strength of the relation sequences are proposed. To evaluate the proposed method, an experiment was performed. A group of social relationships among the university and organizations are constructed. Some social connections are searched using the proposed ranking method. The proposed method is expected to be used to search the association among entities in ontology based knowledge base.

Full Text:



Aleman-Meza, B., Halaschek-Wiener, C., Budak Arpinar, I., Ramakrishnan, C., and Sheth, A., "Ranking complex relationships on the semantic web, "In Proc. of WWW2005, 2005.

Aleman-Meza, B., Nagajan, M., Ramakrishnan, C., Ding, L., Kolari, P., Sheth, A., and Arpinar, I., "Semantic analytics on social networks : Experiences in addressing the problem of conflict of interest detection," In Proc. of WWW2006, 2006.

Anyanwu, K. and Sheth, A., "-Queries : Enabling querying for semantic associations on the Semantic Web," In Proc. of WWW2003, pp. 690-699, 2003.

Anyanwu, K. and Sheth, A., "The operator : Discovering and Ranking associations on the semantic web," SIGMOD, 2002.

Anyanwu, K., Maduko, A., and Sheth, A., "SemRank : Ranking Complex relationship search results on the Semantic Web," In Proc. of WWW, 2005.

Barnaghi, P. M. and Kareen, S. A., "Relation robustness evaluation for the semantic associations," The electronic Journal of Knowledge Management Vol. 5, No. 3, pp. 265-272, 2007.

Faloutsos, C., McCurley, K. S., and Tomkins, A., "Fast Discovery of Connection Subgraphs," In Proc. of the Tenth ACM SIGKDD Conference, 2004.

Lee, W., "A framework for discovering meaningful associations in the annotated life sciences web," Doctoral dissertation, University of Maryland, 2008.

Mark, S. G., "The Strength of Weak Ties," American Journal of Sociology, Vol. 78, No. 6, pp. 1360-1380, 1973.

Miki, T., Nomura, S., and Ishida, T., "Semantic Web link analysis to discover social relationships in academic communities," In Proc. of SAINT'05, 2005.

Oh, S., Ahn, J., and Park, J., "Ontology Selection Ranking Model based on Semantic Similarity Approach," Society for e-Business Studies, Vol. 14, No. 2, pp. 95-116, 2009.

Sicilia, M., Kop, C., and Sartori, F., "Ontology, Conceptualization, and Epistemology for Information Systems, Software Engineering and Service Science," 4th International Workshop ONTOSE2010, 2010.

Thushar, A. K. and Thilagam, P. S., "An approach for discovering the relevant semantic associations in a social network," In Proc. of ADCOM2008, pp. 214-220, 2008.

Travers, T. and Milgram, S., "An Experimental Study of the Small World Problem," Sociometry, Vol. 32, No. 4, p. 425, 1969.

Turney, P. D., "Measuring Semantic Similarity by Latent Relational Analysis," In Proc of the Nineteenth International Joint on Artificial Intelligence(IJCAI-05), pp. 1136-1141, Edinburgh, Scotland, 2005.

Wasserman, S. and Faust, K., "Social Network Analysis," Cambridge, 1994.

Zhang, J., Wang, H., and Sun, Y., "Discovering associations among semantic links, International conference on Web information systems and mining," p. 204, 2009.

Zhuge, H. and Zheng P., "Ranking Semanticlinked Network," In Proc. of WWW2003, 2003.


  • There are currently no refbacks.