Conceptual query expansion

Grootjen, Franc; Weide, Th.P. van der

doi:10.1016/j.datak.2005.03.006

Cited by 64 publications

(36 citation statements)

References 16 publications

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“…Semantic queries are expanded either manually or automatically or interactively. The work on conceptual query expansion for document retrieval is presented in [16]. They use terms of initial result of the user's query as candidates for the query expansion and limit the candidate terms using formal concept analysis.…”

Section: Related Workmentioning

confidence: 99%

WordNet Ontology Based Query Reformulation and Optimization using Disjunctive Clause Elimination

Amshakala¹,

Nedunchezhian²

2011

IJDMS

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Section: Related Workmentioning

confidence: 99%

WordNet Ontology Based Query Reformulation and Optimization using Disjunctive Clause Elimination

Amshakala¹,

Nedunchezhian²

2011

IJDMS

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“…There also exists a large body of research applied to using concepts and ontologies in the retrieval stage only. Concept-based query expansion methods have been implemented using corpus-based methods [Grootjen and van der Weide 2006;Qiu and Frei 1993], domain-specific knowledge sources [Liu and Chu 2005], or an ontology derived from Web sources such as Wikipedia [Milne et al 2007]. But methods based on query expansion, in addition to the aforementioned representation-related issues, are also vulnerable to expansion-specific problems such as query drift and sensitivity to parameter tuning [Billerbeck and Zobel 2004].…”

Section: Related Workmentioning

confidence: 99%

“…Concepts would be best used throughout the entire process, in both indexing and retrieval stages [Gonzalo et al 1998]. A simpler but less accurate solution would apply concept analysis in one stage only, as in concept-based query expansion over BOW retrieval [Grootjen and van der Weide 2006].…”

Section: Introductionmentioning

confidence: 99%

Concept-Based Information Retrieval Using Explicit Semantic Analysis

Egozi

Markovitch

Gabrilovich

2011

ACM Trans. Inf. Syst.

223

122

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Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship between these related keywords may be semantic rather than syntactic, and capturing it thus requires access to comprehensive human world knowledge. Concept-based retrieval methods have attempted to tackle these difficulties by using manually built thesauri, by relying on term cooccurrence data, or by extracting latent word relationships and concepts from a corpus. In this article we introduce a new concept-based retrieval approach based on Explicit Semantic Analysis (ESA), a recently proposed method that augments keywordbased text representation with concept-based features, automatically extracted from massive human knowledge repositories such as Wikipedia. Our approach generates new text features automatically, and we have found that high-quality feature selection becomes crucial in this setting to make the retrieval more focused. However, due to the lack of labeled data, traditional feature selection methods cannot be used, hence we propose new methods that use self-generated labeled training data. The resulting system is evaluated on several TREC datasets, showing superior performance over previous state-of-the-art results.

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“…WordNet [5] is organized in sets of synonyms with the terms of same meaning allowing searches for semantically related nodes. Grootjen et al [6] and Parapar et al [20] used WordNet as a source of additional terms to complement the user's query. Both studies report that no significant improvement was obtained with the expansion.…”

Section: A Query Expansionmentioning

confidence: 99%

Cell assemblies for query expansion in Information Retrieval

Volpe

Moreira

Huyck

2011

The 2011 International Joint Conference on Neural Networks

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Abstract-One of the main tasks in Information Retrieval is to match a user query to the documents that are relevant for it. This matching is challenging because in many cases the keywords the user chooses will be different from the words the authors of the relevant documents have used. Throughout the years, many approaches have been proposed to deal with this problem. One of the most popular consists in expanding the query with related terms with the goal of retrieving more relevant documents. In this paper, we propose a new method in which a Cell Assembly model is applied for query expansion. Cell Assemblies are reverberating circuits of neurons that can persist long beyond the initial stimulus has ceased. They learn through Hebbian Learning rules and have been used to simulate the formation and the usage of human concepts. We adapted the Cell Assembly model to learn relationships between the terms in a document collection. These relationships are then used to augment the original queries. Our experiments use standard Information Retrieval test collections and show that some queries significantly improved their results with our technique.

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Conceptual query expansion

Cited by 64 publications

References 16 publications

WordNet Ontology Based Query Reformulation and Optimization using Disjunctive Clause Elimination

WordNet Ontology Based Query Reformulation and Optimization using Disjunctive Clause Elimination

Concept-Based Information Retrieval Using Explicit Semantic Analysis

Cell assemblies for query expansion in Information Retrieval

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