Jigs and Lures: Associating Web Queries with Structured Entities

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 83–92, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Jigs and Lures: Associating Web Queries with Structured Entities Patrick Pantel Microsoft Research Redmond, WA, USA ppantel@microsoft.com Ariel Fuxman Microsoft Research Mountain View, CA, USA arielf@microsoft.com Abstract We propose methods for estimating the prob- ability that an entity from an entity database is associated with a web search query. Asso- ciation is modeled using a query entity click graph, blending general query click logs with vertical query click logs. Smoothing tech- niques are proposed to address the inherent data sparsity in such graphs, including inter- polation using a query synonymy model. A large-scale empirical analysis of the smooth- ing techniques, over a 2-year click graph collected from a commercial search engine, shows significant reductions in modeling er- ror. The association models are then applied to the task of recommending products to web queries, by annotating queries with products from a large catalog and then mining query- product associations through web search ses- sion analysis. Experimental analysis shows that our smoothing techniques improve cover- age while keeping precision stable, and over- all, that our top-performing model affects 9% of general web queries with 94% precision. 1 Introduction Commercial search engines use query associations in a variety of ways, including the recommendation of related queries in Bing, ‘something different’ in Google, and ‘also try’ and related concepts in Ya- hoo. Mining techniques to extract such query asso- ciations generally fall into four categories: (a) clus- tering queries by their co-clicked url patterns (Wen et al., 2001; Baeza-Yates et al., 2004); (b) leveraging co-occurrences of sequential queries in web search query sessions (Zhang and Nasraoui, 2006; Boldi et al., 2009); (c) pattern-based extraction over lexico- syntactic structures of individual queries (Pas¸ca and Durme, 2008; Jain and Pantel, 2009); and (d) distri- butional similarity techniques over news or web cor- pora (Agirre et al., 2009; Pantel et al., 2009). These techniques operate at the surface level, associating one surface context (e.g., queries) to another. In this paper, we focus instead on associating sur- face contexts with entities that refer to a particu- lar entry in a knowledge base such as Freebase, IMDB, Amazon’s product catalog, or The Library of Congress. Whereas the former models might as- sociate the string “Ronaldinho” with the strings “AC Milan” or “Lionel Messi”, our goal is to associate “Ronaldinho” with, for example, the Wikipedia en- tity page “wiki/AC Milan” or the Freebase entity “en/lionel mess”. Or for the query string “ice fish- ing”, we aim to recommend products in a commer- cial catalog, such as jigs or lures. The benefits and potential applications are large. By knowing the entity identifiers associated with a query (instead of strings), one can greatly improve both the presentation of search results as well as the click-through experience. For example, consider when the associated entity is a product. Not only can we present the product name to the web user, …

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