Learning Semantic Query Suggestions

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An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. We use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features. We apply our method to the task of linking queries from real-world query logs (the transaction logs of the Netherlands Institute for Sound and Vision) to the DBpedia knowledge base. We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over an already high baseline. The resources developed for this paper, i.e., queries, human assessments, and extracted features, are available for download.

  • [PDF] E. Meij, M. Bron, B. Huurnink, L. Hollink, and M. de Rijke, “Learning semantic query suggestions,” in Proceedings of the 8th international conference on the semantic web, 2009.
    [Bibtex]
    @inproceedings{ISWC:2009:Meij,
    Abstract = {Learning Semantic Query Suggestions by Edgar Meij, Marc Bron, Laura Hollink, Bouke Huurnink and Maarten de Rijke is available online now. An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. We use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features. We apply our method to the task of linking queries from real-world query logs (the transaction logs of the Netherlands Institute for Sound and Vision) to the DBpedia knowledge base. We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over an already high baseline. The resources developed for this paper, i.e., queries, human assessments, and extracted features, are available for download. },
    Author = {E. Meij and M. Bron and B. Huurnink and Hollink, L. and de Rijke, M.},
    Booktitle = {Proceedings of the 8th International Conference on The Semantic Web},
    Date-Added = {2011-10-12 18:31:55 +0200},
    Date-Modified = {2012-10-30 08:45:04 +0000},
    Series = {ISWC 2009},
    Title = {Learning Semantic Query Suggestions},
    Year = {2009}}

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