Abstract
In this paper, we use context information extracted from the documents in the collection to improve the performance of the search engine. In first step, we extract context using Lucene, DBPedia-Spotlight, and Wordnet. As the second step, we build a graph using extracted context information. In the third step, in order to group similar contexts, we cluster context graph. In the fourth step, we re-score results using context-clusters and context-information of documents, as well as queries. In the fifth step, we implement a data collection tool to collect gold-standard data. In the sixth and final step, we compare the results of our algorithm with gold-standard data set. According to the experimental results, using context information may improve the search engine performance but the collection should be relatively big.
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References
Apache lucene. http://lucene.apache.org/core/
Apache solr. https://lucene.apache.org/solr/
Mcl—a cluster algorithm for graphs. http://micans.org/mcl/
P.J. Brown, G.J.F. Jones, Context-aware retrieval: exploring a new environment for information retrieval and information filtering. Personal Ubiquitous Comput. 5(4), 253–263 (2001)
C.D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval (Cambridge University Press, Cambridge, 2009)
R. Duzagac, Improving search engine performance with context extraction using lucene, dbpedia-spotlight, and wordnet. Master’s thesis, IK University (2014)
O. Egozi, S. Markovitch, E. Gabrilovich, Concept-based information retrieval using explicit semantic analysis. ACM Trans. Inf. Syst. 29(2), 8:1–8:34 (2011)
A. Kiryakov, B. Popov, I. Terziev, D. Manov, D. Ognyanoff, Semantic annotation, indexing, and retrieval. Web Semant. 2(1), 49–79 (2004)
J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P.N. Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer, C. Bizer, DBpedia—a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 26, 1–58 (2014)
L. Li, L. Zhong, G. Xu, M. Kitsuregawa, A feature-free search query classification approach using semantic distance. Expert Syst. Appl. 39(12), 10,739–10,748 (2012)
R.R. Llinas, I of the Vortex from Neurons to Self (MIT Press, Cambridge, 2002)
G.A. Miller, Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
P. Mylonas, D. Vallet, P. Castells, M. FernÁndez, Y. Avrithis, Personalized information retrieval based on context and ontological knowledge. Knowl. Eng. Rev. 23(1), 73–100 (2008)
M. Schuhmacher, S.P. Ponzetto, Exploiting dbpedia for web search results clustering, in Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, AKBC ’13 (ACM, New York, 2013), pp. 91–96
C. Shaoul, C. Westbury, The westbury lab wikipedia corpus, edmonton, ab: University of alberta. (2010), http://www.psych.ualberta.ca/westburylab/downloads/westburylab.wikicorp.download.html
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© 2014 Springer International Publishing Switzerland
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Düzağaç, R., Yıldız, O.T. (2014). Context Sensitive Search Engine. In: Czachórski, T., Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-09465-6_29
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DOI: https://doi.org/10.1007/978-3-319-09465-6_29
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