Abstract
Query suggestion is proposed to generate alternative queries and help users explore and express their information needs. Most existing query suggestion methods generate query suggestions based on document information or search logs without considering the semantic relationships between the original query and the suggestions. In addition, existing query suggestion diversifying methods generally use greedy algorithm, which has high complexity. To address these issues, we propose a novel query suggestion method to generate semantically relevant queries and diversify query suggestion results based on the WordNet ontology. First, we generate the query suggestion candidates based on Markov random walk. Second, we diversify the candidates according the different senses of original query in the WordNet. We evaluate our method on a large-scale search log dataset of a commercial search engine. The outstanding feature of our method is that our query suggestion results are semantically relevant belonging to different topics. The experimental results show that our method outperforms the two well-known query suggestion methods in terms of precision and diversity with lower time consumption.
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Meij, E., Bron, M., Hollink, L., Huurnink, B., de Rijke, M.: Learning Semantic Query Suggestions. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 424–440. Springer, Heidelberg (2009)
Gong, Z., Cheang, C.W., Hou U, L.: Web Query Expansion by WordNet. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 166–175. Springer, Heidelberg (2005)
Craswell, N., Szummer, M.: Random walks on the click graph. In: SIGIR 2007, pp. 239–246 (2007)
Mei, Q.Z., Zhou, D.Y., Church, K.: Query Suggestion Using Hitting Time. In: CIKM 2008, pp. 469–478 (2008)
Lin, D.: An Information-Theoretic Definition of Similarity. In: ICML 1998, pp. 296–304 (1988)
Beeferman, D., Berger, A.: Agglomerative clustering of a search engine query log. In: KDD 2000, pp. 407–416 (2000)
Cao, H.H., Jiang, D.X., Pei, J., Liao, Z., Chen, E., Li, H.: Context-Aware Query Suggestion by Mining Click-Through and Session Data. In: KDD 2008, pp. 875–884 (2008)
Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A.: The Query-flow Graph: Model and Applications. In: CIKM 2008, pp. 56–63 (2008)
Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A.: An optimization framework for query recommendation. In: WSDM 2010, pp. 609–618 (2010)
Ma, H., Lyu, M.R., King, I.: Diversifying Query Suggestion Results. In: AAAI 2010, pp. 1399–1404 (2010)
Song, Y., Zhou, D., He, L.: Post-Ranking Query Suggestion by Diversifying Search Results. In: SIGIR 2011, pp. 815–824 (2011)
Baraglia, R., Nardini, F., Castillo, C., Perego, R., Donato, D., Silvestri, F.: The effects of time on query flow graph-based models for query suggestion. In: RIAO 2010, pp. 182–189 (2010)
Zhang, Z., Nasraoui, O.: Mining search engine query logs for query recommendation. In: WWW 2006, pp. 1039–1040 (2006)
Sadikov, E., Madhavan, J., Wang, L., Halevy, A.: Clustering Query Refinements by User Intent. In: WWW 2010, pp. 841–850 (2010)
Spink, A., Jansen, B.J.: A Study of Web Search Trends. Webology 1(2) (2004)
Miller, G.A., Beckwith, R., Fellbaum, C.D., Gross, D., Miller, K.: WordNet: An online lexical database. Int. J. Lexicograph. 3(4), 235–244 (1990)
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Zheng, HT., Zhao, J., Zhang, YC., Jiang, Y., Xia, ST. (2014). An Ontology-Based Approach to Query Suggestion Diversification. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_53
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DOI: https://doi.org/10.1007/978-3-319-12640-1_53
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