Automatically Mining Facets for Queries from Their Search Results

We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2016-02, Vol.28 (2), p.385-397
Main Authors: Dou, Zhicheng, Jiang, Zhengbao, Hu, Sha, Wen, Ji-Rong, Song, Ruihua
Format: Article
Language:English
Subjects:
Analogies
Containers
Context
Context modeling
Data mining
Faceted Search
HTML
Lists
Peer-to-peer computing
Queries
Query Facet
Reproduction
Searching
Summarization
Tags
Texts
User Intent
Watches
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Description
Summary:We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by extracting and grouping frequent lists from free text, HTML tags, and repeat regions within top search results. Experimental results show that a large number of lists do exist and useful query facets can be mined by QDMiner. We further analyze the problem of list duplication, and find better query facets can be mined by modeling fine-grained similarities between lists and penalizing the duplicated lists.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2015.2475735