Abstract
In this paper, we propose an evolutionary approach to deal with shortcomings on conventional focused crawling systems in semantic web environment. Thereby, meta-evolution strategy for collaboration among multiple crawlers has to be efficiently carried out. It is based on incremental aggregation of partial semantic structures extracted from web resources, which are in advance annotated with local ontologies. To do this, we employ similarity-based matching algorithm, so that fitness function is formulated by summing all possible semantic similarities. As a result, the best mapping condition (i.e., the fitness is maximized) is obtained for efficiently i) reconciling semantic conflicts between multiple crawlers, and ii) evolving semantic structures of web spaces over time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
De Bra, P.M.E., Post, R.D.J.: Information retrieval in the World-Wide Web: Making client-based searching feasible. Computer Networks and ISDN Systems 27(2), 183–192 (1994)
Chakrabarti, S., van den Berg, M., Dom, B.: Focused crawling: a new approach to topic-specific web resource discovery. Computer Networks 31(11-16), 1623–1640 (1999)
Jung, J.J.: Collaborative web browsing based on semantic extraction of user interests with bookmarks. Journal of Universal Computer Science 11(2), 213–228 (2005)
Liu, H., Milios, E., Janssen, J.: Probabilistic models for focused web crawling. In: Web information and data management (WIDM ’04). Proceedings of the 6th annual ACM international workshop, pp. 16–22. ACM Press, New York (2004)
Jung, J.J., Lee, K.S., Park, S.B., Jo, G.S.: Efficient web browsing with semantic annotation: A case study of product images in e-commerce sites. IEICE Transactions on Information and Systems E88-D(5), 843–850 (2005)
Jung, J.J.: Exploiting semantic annotation to supporting user browsing on the web. Knowledge-Based Systems 20(4), 373–381 (2007)
Jung, J.J.: Ontological framework based on contextual mediation for collaborative information retrieval. Information Retrieval 10(1), 85–109 (2007)
Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-Lite. In: Proceedings of the 16th European Conference on Artificial Intelligence, pp. 333–337 (2004)
Beyer, H.G., Schwefel, H.P.: Evolution strategies - a comprehensive introduction. Natural Computing 1(1), 3–52 (2002)
Cantú-Paz, E.: Order statistics and selection methods of evolutionary algorithms. Information Processing Letters 82(1), 15–22 (2002)
Nguyen, N.T.: Conflicts of ontologies - classification and consensus-based methods for resolving. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 267–274. Springer, Heidelberg (2006)
Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2), 18–28 (2003)
Claypool, M., Brown, D., Le, P., Waseda, M.: Inferring user interest. IEEE Internet Computing 5(6), 32–39 (2001)
Jung, J.J.: An application of collaborative web browsing based on ontology learning from user activities on the web. Computing and Informatics 23(4), 337–353 (2004)
White, R.W., Jose, J.M., Ruthven, I.: An implicit feedback approach for interactive information retrieval. Information Processing and Management 42(1), 166–190 (2006)
Euzenat, J.: Building consensual knowledge bases: context and architecture. In: Proceedings of the 2nd International Conference on Building and Sharing very large-scale Knowledge Bases (KBKS), pp. 143–155. IOS Press, Amsterdam (1995)
Kim, J.: Meta-level patterns for interactive knowledge capture. In: Proceedings of the 3rd International Conference on Knowledge Capture (K-CAP ’05), pp. 207–208. ACM Press, New York (2005)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jung, J.J., Jo, GS., Yeo, SW. (2007). Meta-evolution Strategy to Focused Crawling on Semantic Web. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-540-74695-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74693-5
Online ISBN: 978-3-540-74695-9
eBook Packages: Computer ScienceComputer Science (R0)