A review of ontology based query expansion

Bhogal, Jagdev; MacFarlane, Andy; Smith, Pete

doi:10.1016/j.ipm.2006.09.003

Cited by 315 publications

(143 citation statements)

References 45 publications

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“…The Information Retrieval (IR) community has searched for better methods. A frequently used approach is to expand the query that users enter (Abdelali et al 2007;Bhogal et al 2007). Query expansion helps to prevent the problem of word mismatch (Xu and Croft 1996).…”

Section: Related Workmentioning

confidence: 99%

“…Query expansion helps to prevent the problem of word mismatch (Xu and Croft 1996). Three commonly used methods (Bhogal et al 2007) for query expansion are (i) relevance feedback (Brillakis 2005), (ii) corpus dependent knowledge models (Khan and Khor 2004;Oakes and Taylor 1998) and (iii) corpus independent knowledge models. Corpus independent knowledge models usually use thesauri or ontologies to enrich the user-defined query (Dey et al 2005;Díaz-Galiano et al 2009;Thenmalar and Geetha 2014;UmaDevi and Meera Ghandi 2015;Yadav 2009).…”

Section: Related Workmentioning

confidence: 99%

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From science to practice: Bringing innovations to agronomy and forestry

Willems¹,

Koenderink²,

Top³

2015

JAI

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A B S T R A C TThe challenge of the work presented here is to make innovative research output in the agronomy and forestry domain accessible to end-users, so that it can be practically applied. We have developed an approach that consists of three key-elements: an ontology with domain knowledge, a set of documents that have been annotated and metaannotated, and a system (ask-Valerie) that is based on a dialogue to represent the interaction between end user and system. We show that the dialogue-metaphor is a good way of modelling the interaction between user and system. The system helps the user in formulating his question and in answering it in a useful way. Meta-annotations of key-paragraphs in the document-base turn out to be relevant in assessing in one glance what the content of a document is. End-users are very enthusiastic about the possibilities that ask-Valerie offers them in translating scientific results to their own situation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

From science to practice: Bringing innovations to agronomy and forestry

Willems¹,

Koenderink²,

Top³

2015

JAI

View full text Add to dashboard Cite

show abstract

“…However, in order to apply this method to a retrieval system for which the local context is a conversation, the keyword lists must avoid considering irrelevant topics from ASR errors. Moreover, unlike written documents which follow generally a planned and focused structured, in a conversation users often turn from one topic to another, and adding such a variety of keywords to a query might deteriorate the retrieval results [4,11].…”

Section: Related Workmentioning

confidence: 99%

Query Refinement Using Conversational Context: A Method and an Evaluation Resource

Habibi

Popescu-Belis

2015

Natural Language Processing and Information Systems

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Abstract. This paper introduces a query refinement method applied to queries asked by users during a meeting or a conversation. Current approaches suffer from poor quality to achieve this goal, but we argue that their performance could be improved by focusing on the local context of the conversation. The proposed technique first represents the local context by extracting keywords from the transcript of the conversation. It then expands the queries with keywords that best represent the topic of the query (e.g. pairs of expansion keywords together with a weight indicating their topical similarity to the query). Moreover, we present a dataset called AREX and an evaluation metric. We compared our query expansion approach with other methods, on topics extracted from the AREX dataset and based on relevance judgments collected in a crowdsourcing experiment. The comparisons indicate the superiority of our method on both manual and ASR transcripts of the AMI Meeting Corpus.

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“…Pseudo-relevance feedback methods assume the top-ranked documents to be relevant, but explicit or implicit relevance judgements from users may also be used (Anick, 2003;Keskustalo, Järvelin, & Pirkola, 2008;Vakkari, Jones, Macfarlane, & Sormunen, 2004;Xu & Croft, 1996). The recent interest of the semantic web community regarding models and methods related to ontologies have also sparked a renewed interest in using ontological information for relevance feedback (Bhogal, Macfarlane, & Smith, 2007;Rocha, Schwabe, & Aragao, 2004). In a language modeling setting, local query expansion has been applied to estimate query language models (Lafferty & Zhai, 2003;Tao & Zhai, 2006) or relevance models (Lavrenko & Croft, 2001); we elaborate on the latter in Section 3.…”

Section: Related Workmentioning

confidence: 99%

Conceptual language models for domain-specific retrieval

Meij¹,

Trieschnigg

Rijke³

et al. 2010

Information Processing & Management

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a b s t r a c tOver the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within the language modeling framework, which leverages these conceptual annotations to improve retrieval. By means of relevance feedback the original query is translated into a conceptual representation, which is subsequently used to update the query model.Extensive experimental work on five test collections in two domains shows that our approach gives significant improvements in terms of recall, initial precision and mean average precision with respect to a baseline without relevance feedback. On one test collection, it is also able to outperform a text-based pseudo-relevance feedback approach based on relevance models. On the other test collections it performs similarly to relevance models. Overall, conceptual language models have the added advantage of offering query and browsing suggestions in the form of conceptual annotations. In addition, the internal structure of the meta-language can be exploited to add related terms.Our contributions are threefold. First, an extensive study is conducted on how to effectively translate a textual query into a conceptual representation. Second, we propose a method for updating a textual query model using the concepts in conceptual representation. Finally, we provide an extensive analysis of when and how this conceptual feedback improves retrieval.

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A review of ontology based query expansion

Cited by 315 publications

References 45 publications

From science to practice: Bringing innovations to agronomy and forestry

From science to practice: Bringing innovations to agronomy and forestry

Query Refinement Using Conversational Context: A Method and an Evaluation Resource

Conceptual language models for domain-specific retrieval

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