Abstract
Today, a vast amount of information is made available over the Web in the form of unstructured text indexed by Web search engines. But especially for searches on abstract concepts or context terms, a simple keyword-based Web search may compromise retrieval quality, because query terms may or may not directly occur in the texts (vocabulary problem). The respective state-of-the-art solution is query expansion leading to an increase in recall, although it often also leads to a steep decrease of retrieval precision. This decrease however is a severe problem for digital library providers: in libraries it is vital to ensure high quality retrieval meeting current standards. In this paper we present an approach allowing even for abstract context searches (conceptual queries) with high retrieval quality by using Wikipedia to semantically bridge the gap between query terms and textual content. We do not expand queries, but extract the most important terms from each text document in a focused Web collection and then enrich them with features gathered from Wikipedia. These enriched terms are further used to compute the relevance of a document with respect to a conceptual query. The evaluation shows significant improvements over query expansion approaches: the overall retrieval quality is increased up to 74.5% in mean average precision.
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
Furnas, G.W., et al.: The vocabulary problem in human-system communication. Communications of the ACM 30(11), 964–971 (1987)
Kraft, R., Zien, J.: Mining anchor text for query refinement. In: Proc. of Int. Conf. on World Wide Web, WWW (2004)
Köhncke, B., Balke, W.-T.: Using Wikipedia categories for compact representations of chemical documents. In: Proc. of Int. Conf. on Information and Knowledge Management, CIKM (2010)
Carpineto, C., Romano, G.: A Survey of Automatic Query Expansion in Information Retrieval. ACM Computing Surveys 44(1), 1–50 (2012)
Lüke, T., Schaer, P., Mayr, P.: Improving Retrieval Results with discipline-specific Query Expansion. In: Zaphiris, P., Buchanan, G., Rasmussen, E., Loizides, F. (eds.) TPDL 2012. LNCS, vol. 7489, pp. 408–413. Springer, Heidelberg (2012)
Xu, J., Croft, W.: Query expansion using local and global document analysis. In: Proc. of Int. Conf. on Research and Development in Information Retrieval, SIGIR (1996)
Jing, Y., Croft, W.: An association thesaurus for information retrieval. In: Proceedings of RIAO, pp. 1–15 (1994)
Cao, G., et al.: Selecting good expansion terms for pseudo-relevance feedback. In: Proc. of Int. Conf. on Research and Development in Information Retrieval, SIGIR (2008)
Metzler, D., Croft, W.B.: Latent concept expansion using markov random fields. In: Proc. of Int. Conf. on Research and Development in Information Retrieval, SIGIR (2007)
Cui, H., Wen, J., Nie, J., Ma, W.: Query expansion by mining user logs. IEEE Transactions on Knowledge and Data Engineering 15(4), 829–839 (2003)
Wang, X., Zhai, C.X.: Mining term association patterns from search logs for effective query reformulation. In: Proc. of Int. Conf. on Information and Knowledge Management, CIKM (2008)
Gao, J., Nie, J.: Towards Concept-Based Translation Models Using Search Logs for Query Expansion. In: Proc. of Int. Conf. on Inf. and Knowledge Management, CIKM (2012)
Deerwester, S., et al.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41(6), 391–407 (1998)
Hu, J., Wang, G., Lochovsky, F., Sun, J., Chen, Z.: Understanding user’s query intent with wikipedia. In: Proc. of Int. Conf. on World Wide Web, WWW (2009)
Xu, Y., et al.: Query dependent pseudo-relevance feedback based on wikipedia. In: Proc. of Int. Conf. on Research and Development in Information Retrieval, SIGIR (2009)
Bendersky, M., et al.: Effective query formulation with multiple information sources. In: Proc. of Int. Conf. on Web Search and Data Mining, WSDM (2012)
Milne, D., et al.: A knowledge-based search engine powered by wikipedia. In: Proc. of Int. Conf. on Information and Knowledge Management, CIKM (2007)
Surowiecki, J.: The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business. Economies, Societies and Nations (2004)
Milne, D., Witten, I.H.: An open-source toolkit for mining Wikipedia. Artificial Intelligence 194, 222–239 (2012)
Köhncke, B., Balke, W.-T.: Context-Sensitive Ranking Using Cross-Domain Knowledge for Chemical Digital Libraries. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, C.J. (eds.) TPDL 2013. LNCS, vol. 8092, pp. 285–296. Springer, Heidelberg (2013)
Sahlgren, M.: An Introduction to Random Indexing. In: Proc. of the Methods and Applications of Semantic Indexing Workshop (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Köhncke, B., Siehndel, P., Balke, WT. (2013). Bridging the Gap – Using External Knowledge Bases for Context-Aware Document Retrieval. In: Urs, S.R., Na, JC., Buchanan, G. (eds) Digital Libraries: Social Media and Community Networks. ICADL 2013. Lecture Notes in Computer Science, vol 8279. Springer, Cham. https://doi.org/10.1007/978-3-319-03599-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-03599-4_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03598-7
Online ISBN: 978-3-319-03599-4
eBook Packages: Computer ScienceComputer Science (R0)