article

Leveraging semantic resources in diversified query expansion

Authors:

Sameep MehtaAuthors Info & Claims

World Wide Web, Volume 21, Issue 4

Pages 1041 - 1067

https://doi.org/10.1007/s11280-017-0468-7

Published: 01 July 2018 Publication History

Abstract

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. In this paper, we consider the usage of semantic resources and tools to arrive at improved methods for diversified query expansion. In particular, we develop two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively. Both the approaches operate on a common three-phase framework; that of first taking a set of informative terms from the search results of the initial query, then building a graph, following by using a diversity-conscious node ranking to prioritize candidate terms for diversified query expansion. Our methods differ in the second phase, with the first method Select-Link-Rank (SLR) linking terms with Wikipedia entities to accomplish graph construction; on the other hand, our second method, Select-Embed-Rank (SER), constructs the graph using similarities between distributional word embeddings. Through an empirical analysis and user study, we show that SLR ourperforms state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversified query expansion. Our empirical analysis also illustrates that SER outperforms the baselines convincingly, asserting that it is the best available method for those cases where SLR is not applicable; these include narrow-focus search systems where a relevant knowledge base is unavailable. Our SLR method is also seen to outperform a state-of-the-art method in the task of diversified entity ranking.

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Index Terms

Leveraging semantic resources in diversified query expansion
1. Computing methodologies
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

Index terms have been assigned to the content through auto-classification.

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cover image World Wide Web

World Wide Web Volume 21, Issue 4

Jul 2018

401 pages

ISSN:1386-145X

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2018

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https://dl.acm.org/doi/10.1109/TKDE.2023.3295803
Abdollahi SKuculo TGottschalk S(2024)Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge GraphAdvances in Information Retrieval10.1007/978-3-031-56060-6_22(333-348)Online publication date: 24-Mar-2024
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