Krisztian Balog
Robert Neumayer
ABSTRACT
A significant portion of information needs in web search target entities. These may come in different forms or flavours, ranging from short keyword queries to more verbose requests, expressed in natural language. We address the task of automatically annotating queries with target types from an ontology. The identified types can subsequently be used, e.g., for creating semantically more informed query and retrieval models, filtering results, or directing the requests to specific verticals. Our study makes the following contributions. First, we formalise the task of hierarchical target type identification, argue that it is best viewed as a ranking problem, and propose multiple evaluation metrics. Second, we develop a purpose-built test collection by hand-annotating over 300 queries, from various recent entity search benchmarking campaigns, with target types from the DBpedia ontology. Finally, we introduce and examine two baseline models, inspired by federated search techniques. We show that these methods perform surprisingly well when target types are limited to a flat list of top level categories; finding the right level of granularity in the hierarchy, however, is particularly challenging and requires further investigation.
- J. Arguello, F. Diaz, J. Callan, and J.-F. Crespo. Sources of evidence for vertical selection. In Proc. of SIGIR '09, pages 315--322, 2009. Google Scholar
Digital Library
- K. Balog, L. Azzopardi, and M. de Rijke. A language modeling framework for expert finding. Inf. Process. Man., 45(1):1--19, 2009. Google ScholarDigital Library
- K. Balog, M. Bron, and M. De Rijke. Query modeling for entity search based on terms, categories, and examples. ACM Trans. Inf. Syst., 29:22:1--22:31, 2011. Google ScholarDigital Library
- K. Balog, P. Serdyukov, and A. P. de Vries. Overview of the TREC 2011 entity track. In Proc. TREC '11, 2012.Google Scholar
- S. M. Beitzel, E. C. Jensen, O. Frieder, D. D. Lewis, A. Chowdhury, and A. Kolcz. Improving automatic query classification via semi-supervised learning. In Proc. of ICDM '05, pages 42--49, 2005. Google ScholarDigital Library
- M. Bendersky, W. B. Croft, and D. A. Smith. Structural annotation of search queries using pseudo-relevance feedback. In Proc. of CIKM '10, pages 1537--1540, 2010. Google ScholarDigital Library
- R. Blanco, H. Halpin, D. M. Herzig, P. Mika, J. Pound, H. S. Thompson, and T. T. Duc. Entity search evaluation over structured web data. In Proc. of EOS '11, 2011.Google Scholar
- M. Bron, K. Balog, and M. de Rijke. Ranking related entities: Components and analyses. In Proc. of CIKM '10, pages 1079--1088, 2010. Google ScholarDigital Library
- G. Demartini, T. Iofciu, and A. de Vries. Overview of the inex 2009 entity ranking track. In Focused Retrieval and Evaluation, volume 6203, pages 254--264. 2010. Google ScholarDigital Library
- J. L. Elsas, J. Arguello, J. Callan, and J. G. Carbonell. Retrieval and feedback models for blog feed search. In Proc. of SIGIR'08, pages 347--354. ACM, 2008. Google ScholarDigital Library
- L. Gravano, V. Hatzivassiloglou, and R. Lichtenstein. Categorizing web queries according to geographical locality. In Proc. of CIKM'03, pages 325--333, 2003. Google ScholarDigital Library
- J. Guo, G. Xu, X. Cheng, and H. Li. Named entity recognition in query. In Proc. of SIGIR '09, pages 267--274, 2009. Google ScholarDigital Library
- B. J. Jansen and D. Booth. Classifying web queries by topic and user intent. In Proc. of CHI EA '10, pages 4285--4290, 2010. Google ScholarDigital Library
- I.-H. Kang and G. Kim. Query type classification for web document retrieval. In Proc. of SIGIR '03, pages 64--71, 2003. Google ScholarDigital Library
- R. Kaptein, P. Serdyukov, A. De Vries, and J. Kamps. Entity ranking using wikipedia as a pivot. In Proc. of CIKM '10, pages 69--78, 2010. Google ScholarDigital Library
- X. Li, Y.-Y. Wang, and A. Acero. Learning query intent from regularized click graphs. In Proc. of SIGIR '08, pages 339--346, 2008. Google ScholarDigital Library
- Y. Li, Z. Zheng, and H. K. Dai. Kdd cup-2005 report: facing a great challenge. SIGKDD Explor. Newsl., 7(2):91--99, 2005. Google ScholarDigital Library
- . Manshadi and X. Li. Semantic tagging of web search queries. In Proc. of ACL '09, pages 861--869, 2009. Google ScholarDigital Library
- M. Paşca. Weakly-supervised discovery of named entities using web search queries. In Proc. of CIKM '07, pages 683--690, 2007. Google ScholarDigital Library
- J. Pound, P. Mika, and H. Zaragoza. Ad-hoc object retrieval in the web of data. In Proc. of WWW '10, pages 771--780, 2010. Google ScholarDigital Library
- S. Schlobach, M. Olsthoorn, and M. De Rijke. Type checking in open-domain question answering. In Proc. of ECAI '04, pages 398--402, 2004.Google Scholar
- J. Seo and W. B. Croft. Blog site search using resource selection. In Proc. of CIKM '08, pages 1053--1062, 2008. Google ScholarDigital Library
- A. Singh and K. Visweswariah. Cqc: classifying questions in cqa websites. In Proc. of CIKM '11, pages 2033--2036, 2011. Google ScholarDigital Library
- B. Tan and F. Peng. Unsupervised query segmentation using generative language models and wikipedia. In Proc. of WWW '08, pages 347--356, 2008. Google ScholarDigital Library
- D. Vallet and H. Zaragoza. Inferring the most important types of a query: a semantic approach. In Proc. of SIGIR '08, pages 857--858, 2008. Google ScholarDigital Library
- K. Zhou, R. Cummins, M. Halvey, M. Lalmas, and J. M. Jose. Assessing and predicting vertical intent for web queries. In Proc. of ECIR '12, pages 499--502, 2012. Google ScholarDigital Library
Index Terms
- Hierarchical target type identification for entity-oriented queries
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