Mining Facets For Queries From Their Search Results
A query facet is a set of items which describe and summarize one important aspect of a query. Here a facet item is typically a word or a phrase. A query may have multiple facets that summarize the information about the query from different perspectives. For the query “watches”, its query facets cover the knowledge about watches in five unique aspects, including brands, gender categories, supporting features, styles, and colors. The query “visit Beijing” has a facet about popular resorts in Beijing (Tiananmen square, forbidden city, summer palace, …) and a facet on several travel-related topics (attractions, shopping, dining, …). Query facets provide interesting and useful knowledge about a query and thus can be used to improve search experiences in many ways. First, we can display query facet together with the original search results in an appropriate way. Thus, users can understand some important facets of a query without browsing tens of pages. For example, a user could learn different brands and categories of watches. We can also implement a faceted search based on query facets. User can clarify their specific intent by selecting facet items. Then search results could be restricted to the documents that are relevant to the items. These multiple groups of query facets are in particular useful for vague or ambiguous queries, such as “apple”. We could show the products of Apple Inc. in one facet and different types of the fruit apple in another. Second, query facets may provide direct information or instant answers that users are seeking. For example, for the query “lost season 5”, all episode titles are shown in one facet and main actors are shown in another. In this case, displaying query facets can save browsing time. Third, query facets may also be used to improve the diversity of the ten blue links. We can re-rank search results to avoid showing the pages that are near-duplicated in query facets at the top. Query facets also contain structured knowledge covered by or related to the input keywords of a query, and thus they can be used in many other fields besides traditional web search, such as semantic search or entity search. There has been a lot of recent work on automatically building knowledge ontology on the Web . Query facets can become a possible data source for this. We observe that important pieces of information about a query are usually presented in list styles and repeated many times among top retrieved documents. Thus we propose aggregating frequent lists within the top search results to mine query facets and implement a system called QDMiner. More specifically, QDMiner extracts lists from free text, HTML tags, and repeat regions contained in the top search results, groups them into clusters based on the items they contain, then ranks the clusters and items based on how the lists and items appear in the top results. We propose two models, the Unique Website Model and the Context Similarity Model, to rank query facets. In the Unique Website Model, we assume that lists from the same website might contain duplicated information, whereas different websites are independent and each can contribute a separated vote for weighting facets. However, we find that sometimes two lists can be duplicated, even if they are from different websites.
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