Abstract
Traditional web search engines are keyword-based. Such a mechanism is effective when the user knows exactly the right words in the web pages they are looking for. However, it doesn’t produce good results if the user asks for a concept or topic that has broader and sometimes ambiguous meanings. In this paper, we present a framework that improves web search experiences through the use of a probabilistic knowledge base. The framework classifies web queries into different patterns according to the concepts and entities in addition to keywords contained in these queries. Then it produces answers by interpreting the queries with the help of the knowledge base. Our preliminary results showed that the new framework is capable of answering various types of concept-based queries with much higher user satisfaction, and is therefore a valuable addition to the traditional web search.
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References
Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD (2012)
Song, Y., Wang, H., Wang, Z., Li, H., Chen, W.: Short text conceptualization using a probabilistic knowledgebase. In: IJCAI (2011)
Lee, T., Wang, Z., Wang, H., Hwang, S.: Web scale taxonomy cleansing. In: VLDB (2011)
Zhang, Z., Zhu, K.Q., Wang, H.: A system for extracting top-k lists from the web. In: KDD (2012)
Liu, X., Song, Y., Liu, S., Wang, H.: Automatic taxonomy construction from keywords. In: KDD (2012)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD (2008)
Fellbaum, C. (ed.): WordNet: an electronic lexical database. MIT Press (1998)
Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: COLING, pp. 539–545 (1992)
Wang, Y.: Supplementary material for topic search on the web (2010), http://research.microsoft.com/en-us/projects/probase/topicsearch.aspx
Hakia: Hakia (2011), http://www.hakia.com
Evri: Evri (2011), http://www.evri.com
SenseBot: Sensebot (2011), http://www.sensebot.net
Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM Journal on Discrete Mathematics 17, 134–160 (2003)
Powerset: Powerset (2011), http://www.powerset.com
Alpha, W.: Wolfram alpha (2011), http://www.wolframalpha.com
DeepDyve: Deepdyve (2011), http://www.deepdyve.com
Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW, pp. 727–736 (2006)
Haveliwala, T.H.: Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)
Guo, J., Xu, G., Cheng, X., Li, H.: Named entity recognition in query. In: SIGIR, pp. 267–274 (2009)
Lesk, M.E.: Word-word associations in document retrieval systems. American Documentation 20, 27–38 (1969)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. JASIS 41(4), 288–297 (1990)
Fonseca, B.M., Golgher, P.B., Pôssas, B., Ribeiro-Neto, B.A., Ziviani, N.: Concept-based interactive query expansion. In: CIKM, pp. 696–703 (2005)
Jones, R., Fain, D.C.: Query word deletion prediction. In: SIGIR, pp. 435–436 (2003)
Kumaran, G., Carvalho, V.R.: Reducing long queries using query quality predictors. In: SIGIR, pp. 564–571 (2009)
Durham, I., Lamb, D.A., Saxe, J.B.: Spelling correction in user interfaces. Commun. ACM 26(10), 764–773 (1983)
Radlinski, F., Broder, A.Z., Ciccolo, P., Gabrilovich, E., Josifovski, V., Riedel, L.: Optimizing relevance and revenue in ad search: a query substitution approach. In: SIGIR, pp. 403–410 (2008)
Antonellis, I., Garcia-Molina, H., Chang, C.C.: Simrank++: Query rewriting through link analysis of the click graph. In: VLDB (June 2008)
Li, P., Church, K.: A sketch algorithm for estimating two-way and multi-way associations. Computational Linguistics 33(3), 305–354 (2007)
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Wang, Y., Li, H., Wang, H., Zhu, K.Q. (2012). Concept-Based Web Search. In: Atzeni, P., Cheung, D., Ram, S. (eds) Conceptual Modeling. ER 2012. Lecture Notes in Computer Science, vol 7532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34002-4_35
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DOI: https://doi.org/10.1007/978-3-642-34002-4_35
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