Discovering Relevant Semantic Associations Based on User Specified Context
(*) Corresponding author
Semantic associations are the complex relationships that exist between two entities in an RDF knowledge base. As the complexity and size of ontologies are increasing rapidly, the numbers of semantic associations are becoming increasingly overwhelming between a pair of entities. Hence, ranking of semantic associations is required in order to present more relevant semantic associations to the user. One of the criteria to find semantic associations is based on the context. Existing systems allow the user to define context by selecting concepts or regions from the ontology only at the level of schema but they do not take into account defining the context at the instance (or entity) level. As there exist a large number of instances for a schema concept in the knowledgebase, defining the context at the instance level may help the user in retrieving useful associations. In addition to this, present systems do not allow the user to retrieve the semantic associations based on his/her interested relationship though relationships are the important components of the Semantic Web. Due to this, sometimes user gets too many associations which require further investigation to get desired associations. To overcome these problems, this paper proposes a novel method to retrieve relevant semantic associations. Specifically, it proposes two parameters namely, Entity weight (which capture user’s interest at the instance level) and Relationship weight (which capture user’s interest on relationships) which are coupled with the context (defined at the schema level) can define user’s domain of interest more effectively thus produce user interested semantic associations. To make obvious the effectiveness of the proposed method, SWETO data set is used and the results demonstrate that the proposed method retrieves relevant semantic associations than the existing methods.
Copyright © 2015 Praise Worthy Prize - All rights reserved.
Berners-Lee, T., Hendler, J., and Lassila, O. (2001). "The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities." Scientific American, 284(5), 34.
Guha, R. V., and McCool, R. (2003). "TAP: A Semantic Web Test-bed." Journal of Web Semantics, 1(1), 81-87.
Aleman-Meza, B., Halaschek, C., Sheth, A., Arpinar, I. B., and Sannapareddy, G. "SWETO: Large-Scale Semantic Web Test-bed." 16th International Conference on Software Engineering and Knowledge Engineering (SEKE2004): Workshop on O1ntology in Action, Banff, Canada, 490-493.
Sheth, A. P., Arpinar, I. B., and Kashyap, V. (2003). "Relationships at the Heart of Semantic Web: Modelling, Discovering and Exploiting Complex Semantic Relationships." Technical Report, LSDIS Lab, Computer Science, University of Georgia, Athens GA 30622, 2002.
Anyanwu Kemafor, Sheth Amit. ρ-operator: Discovering and Ranking Semantic Associations on the Semantic Web, ACM SIGMOD Record, v. 31 n.4, December 2002.
Anyanwu Kemafor, Angela Maduko, Sheth Amit. SemRank: ranking complex relationship search results on the Semantic Web, in: Proc. of the 14th International World Wide Web Conference, ACM Press, 2005, pp. 117–127.
Shahdad Shariatmadari, Ali Mamat, Ibrahim Hamidah, Mustapha Norwati (2008). SwSim:Discovering semantic similarity association in semantic web. Proceedings of International Symposium on ITSim 2008, 1-4.
M.-E. Vidal, L. Rashid, L. Ibabez, J. Rivera, H. Rodrogiez, E. Ruckhaus, A ranking-based approach to discover semantic association between linked data, in: The 2nd International Workshop on Inductive Reasoning and Machine learning for the Semantic Web, 2010, pp.18-29.
Aleman-Meza, B., Halaschek, C., Arpinar, I. B., and Sheth, A. "Context-Aware Semantic Association Ranking." First International Workshop on Semantic Web and Databases, Berlin, Germany, 33-50.
Aleman-Meza Bonerges, Halaschek-Wiener Christian, Arpinar IB, Ramakrishnan Cartic, Sheth Amit (2005). Ranking Complex Relationships on the Semantic Web. IEEE Internet Computing 9(3); 37-44. Doi:10.1109/MIC.200.63.
Sheth, A. P., Aleman-Meza, B., Arpinar, I. B., Halaschek, C., Ramakrishnan, C., Bertram, C., Warke, Y., Avant, D., Arpinar, F. S., Anyanwu, K., and Kochut, K. (2005a). "Semantic Association Identification and Knowledge Discovery for National Security Applications." Journal of Database Management, 16(1), 33-53.
Halaschek, C., Aleman-Meza, B., Arpinar, I. B., and Sheth, A. P. "Discovering and Ranking Semantic Associations over a Large RDF Metabase." 30th International Conference on Very Large Data Bases, Toronto, Canada.
Myungjin Lee, Wooju Kim. Semantic association search and rank method based on spreading activation for the Semantic Web, in: IEEE International Conference on Industrial Engineering and Engineering Management, 2009, pp. 1523.
Myungjin Lee, Wooju Kim, Sangun Park. Searching and ranking method of relevant resources by user intention on the Semantic Web, Expert Systems with Applications 39 (2012) 4111–4121.
Viswanathan V, Ilango K. Ranking semantic relationships between two entities using personalization in contest specification. Information Sciences, Elsevier, 207 (2012) 35-49.
Lassila Ora and Swick R. Resource Description Framework (RDF) Model and Syntax Specification, W3C Recommendation. 1999
Brickley D and Guha RV. Resource Description Framework (RDF) Schema Specification 1.0, W3C Candidate Recommendation. 2000.
Web Ontology Language, http://www.w3.org/2004/OWL/, 2004.
Diaconis P, Graham R: Spearman’s footrule as a measure of disarray, Journal of the Royal Statistical Society Series B 39 (2) (1977) 262–268.
Djaghloul, Y., Boufaida, Z., Toward Peer to Peer Platform Integration based on OWL Ontology and Roaming Service, (2014) International Journal on Information Technology (IREIT), 2 (6), pp. 195-206.
Benharzallah, S., Kazar, O., Intelligent Agents for a Semantic Mediation of Information Systems, (2013) International Journal on Information Technology (IREIT), 1 (1), pp. 31-36.
Nagarajan, G., Thyagharajan, K.K., Rule-based semantic content extraction in image using fuzzy ontology, (2014) International Review on Computers and Software (IRECOS), 9 (2), pp. 266-277.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize