Concept-based interactive query expansion

Fonseca, Bruno M.; Golgher, Paulo B.; Pôssas, Bruno; Ribeiro-Neto, Berthier; Ziviani, Nívio

doi:10.1145/1099554.1099726

Cited by 160 publications

(80 citation statements)

References 14 publications

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“…Interactive Query Refinement (IQE): IQE [8] starts with a query that represents a fully-specified information need and attempts to find meaningful specializations, most commonly, as words to augment the query. For example, 'world cup' could be an initial query whose refinement suggestions by an IQE system may include terms such as football and 2010.…”

Section: Related Workmentioning

confidence: 99%

Query Suggestions for Textual Problem Solution Repositories

Deepak

Chakraborti

Khemani

2013

Lecture Notes in Computer Science

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Abstract. Textual problem-solution repositories are available today in various forms, most commonly as problem-solution pairs from community question answering systems. Modern search engines that operate on the web can suggest possible completions in real-time for users as they type in queries. We study the problem of generating intelligent query suggestions for users of customized search systems that enable querying over problem-solution repositories. Due to the small scale and specialized nature of such systems, we often do not have the luxury of depending on query logs for finding query suggestions. We propose a retrieval model for generating query suggestions for search on a set of problem solution pairs. We harness the problem solution partition inherent in such repositories to improve upon traditional query suggestion mechanisms designed for systems that search over general textual corpora. We evaluate our technique over real problem-solution datasets and illustrate that our technique provides large and statistically significant improvements over the state-of-the-art technique in query suggestion.

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Section: Related Workmentioning

confidence: 99%

Query Suggestions for Textual Problem Solution Repositories

Deepak

Chakraborti

Khemani

2013

Lecture Notes in Computer Science

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“…Work in this space can be collectively referred to as query rewriting. The relevant techniques include query parsing [19], query expansion [20][21][22], query reduction [23,24], spelling correction [25] and query substitution [26,27].…”

Section: Related Workmentioning

confidence: 99%

Concept-Based Web Search

Wang

et al. 2012

Conceptual Modeling

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Abstract. Traditional web search engines are keyword-based. Such a mechanism is effective when the user knows exactly the right words in the web pages they are looking for. However, it doesn't produce good results if the user asks for a concept or topic that has broader and sometimes ambiguous meanings. In this paper, we present a framework that improves web search experiences through the use of a probabilistic knowledge base. The framework classifies web queries into different patterns according to the concepts and entities in addition to keywords contained in these queries. Then it produces answers by interpreting the queries with the help of the knowledge base. Our preliminary results showed that the new framework is capable of answering various types of concept-based queries with much higher user satisfaction, and is therefore a valuable addition to the traditional web search.

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“…Retrieval is then restricted to documents relevant to these subgraphs. This is a form of query expansion similar to the method of [18]: the selection of documents of the starting corpus C corresponds to a top-level query; this query is expanded by the information from the subgraph and the time restriction. STORIES then uses all the nodes n as a query (restriction) for the documents inside c i to obtain the pertinent document subset, as identified by a search over a Lucene index.…”

Section: Related Workmentioning

confidence: 99%

STORIES in Time: A Graph-Based Interface for News Tracking and Discovery

Berendt¹,

Subasic²

2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

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Abstract-We present the STORIES methods and tool for (a) learning an abstracted story representation from a collection of time-indexed documents; (b) visualising it in a way that encourages users to interact and explore in order to discover temporal "story stages" depending on their interests; and (c) supporting the search for documents and facts that pertain to the user-constructed story stages. In addition, we give an overview of evaluation studies of the tool.

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Concept-based interactive query expansion

Cited by 160 publications

References 14 publications

Query Suggestions for Textual Problem Solution Repositories

Query Suggestions for Textual Problem Solution Repositories

Concept-Based Web Search

STORIES in Time: A Graph-Based Interface for News Tracking and Discovery

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