Abstract
Traditional Web (Web 1.0) is a web of documents. Finding documents is the main goal of information retrieval. There were some improvements in IR (Information Retrieval) on the Web since tf-idf (term frequency inverse document frequency) concerning using other information than just documents themselves. One of those approaches is analyzing link structure used in HITS and Google PageRank. Another approach may be using time metadata to enable filtering based on document publishing date as used e.g. in Google Blog Search. In this paper a Web IR method using relationship metadata and clustering is presented.
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
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284, 28–37 (2001)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46, 604–632 (1998)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30, 107–117 (1998)
Kopel, M., Zgrzywa, A.: The consistency and conformance of web document collection based on heterogeneous DAC graph. New Frontiers in Applied Artificial Intelligence, 321–330 (2008)
Kopel, M., Zgrzywa, A.: Application of Agent-Based personal web of trust to local document ranking. Agent and Multi-Agent Systems: Technologies and Applications, 288–297 (2007)
Dongen, S.: Graph clustering by flow simulation. PhD dissertation. University of Utrecht (2000)
Gordon, M., Pathak, P.: Finding information on the world wide web: the retrieval effectiveness of search engines. Inf. Process. Manage. 35, 141–180 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kopel, M., Zgrzywa, A. (2011). Search Result Clustering Using Semantic Web Data. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-20042-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20041-0
Online ISBN: 978-3-642-20042-7
eBook Packages: Computer ScienceComputer Science (R0)