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Many search engine users attempt to satisfy an information need by issuing multiple queries, with the expectation that each result will contribute some portion of the required information. Previous research has shown that structured or semi-structured descriptive knowledge bases (such as Wikipedia) can be used to improve search quality and experience for general or entity-centric queries. However, such resources do not have sufficient coverage of procedural knowledge, i.e. what actions should be performed and what factors should be considered to achieve some goal; such procedural knowledge is crucial when responding to task-oriented search queries. This paper provides a first attempt to bridge the gap between two evolving research areas: development of procedural knowledge bases (such as wikiHow) and task-oriented search. We investigate whether task-oriented search can benefit from existing procedural knowledge (search task suggestion) and whether automatic procedural knowledge construction can benefit from users' search activities (automatic procedural knowledge base construction). We propose to create a three-way parallel corpus of queries, query contexts, and task descriptions, and reduce both problems to sequence labeling tasks. We propose a set of textual features and structural features to identify key search phrases from task descriptions, and then adapt similar features to extract wikiHow-style procedural knowledge descriptions from search queries and relevant text snippets. We compare our proposed solution with baseline algorithms, commercial search engines, and the (manually-curated) wikiHow procedural knowledge; experimental results show an improvement of +0.28 to +0.41 in terms of email protected and mean average precision (MAP).
Yang et al. (Tue,) studied this question.