Understanding Information Needs [chapter]

Krisztian Balog
2018 Advanced Topics in Information Retrieval  
Understanding what the user is looking for is at the heart of delivering a quality search experience. After all, it is rather difficult to serve good results, unless we can comprehend the intent and meaning behind the user's query. Query understanding is the first step that takes place before the scoring of results. Its overall aim is to infer a semantically enriched representation of the information need. This involves, among others, classifying the query according to higher-level goals or
more » ... nt, segmenting it into parts that belong together, interpreting the query structure, recognizing and disambiguating the mentioned entities, and determining if specific services or verticals 1 should be invoked. Such semantic analysis of queries has been a longstanding research area in information retrieval. In Sect. 7.1, we give a brief overview of IR approaches to query understanding. In the rest of the chapter, we direct our focus of attention to representing information needs with the help of structured knowledge repositories. The catchphrase "things, not strings" was coined by Google when introducing their Knowledge Graph. 2 It aptly describes the current chapter's focus: Capturing what the query is about by automatically annotating it with entries from a knowledge repository. These semantic annotations can then be utilized in downstream processing for result ranking (see Chap. 4) and/or result presentation. Specifically, in Sect. 7.2, we seek to identify the types or categories of entities that are targeted by the query. In Sect. 7.3, we perform entity linking in queries, which is about recognizing specific entity mentions and annotating them with unique identifiers from the underlying knowledge repository. Additionally, we consider the case of unresolvable ambiguity, when queries have multiple possible interpretations. 1 A vertical is a specific segment of online content. Some of the most common verticals include shopping, travel, job search, the automotive industry, medical information, and scholarly literature. 2 https://googleblog.blogspot.no/2012/05/introducing-knowledge-graph-things-not.html.
doi:10.1007/978-3-319-93935-3_7 fatcat:kbqo7rhshrcivlfsauptk3z3oa