User centered and ontology based information retrieval system for life sciences

Sy, Mohameth-François; Ranwez, Sylvie; Montmain, Jacky; Régnault, Armelle; Crampes, Michel; Ranwez, Vincent

doi:10.1186/1471-2105-13-s1-s4

Cited by 33 publications

(21 citation statements)

References 37 publications

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“…Applications to provide inexact search capabilities over ontologies or to improve classical information retrieval techniques have also been proposed, e.g., (Hliaoutakis, 2005;Varelas et al, 2005;Hliaoutakis et al, 2006;Kiefer et al, 2007;Sy et al, 2012;Pirró, 2012). In this context, semantic measures have also been successfully applied to learning tasks using Semantic Web technologies (D'Amato, 2007).…”

Section: Knowledge Engineering Semantic Web and Linked Datamentioning

confidence: 99%

“…They can therefore be used to improve classic models, e.g., synonyms will no longer be considered as totally different words. As an example, semantic measures have been successfully used in the design of ontology-based information retrieval systems and for query expansion, e.g., (Hliaoutakis, 2005;Hliaoutakis et al, 2006;Varelas et al, 2005;Baziz et al, 2007;Saruladha et al, 2010b;Sy et al, 2012).…”

Section: Other Applicationsmentioning

confidence: 99%

“…An important aspect is that semantic measures based on ontologies allow for the analysis and querying of non-textual resources and therefore do not restrict IR techniques in text analysis, e.g., genes annotated by concepts can be queried (Sy et al, 2012). In the same vein, the definition of efficient generic indexing solutions based on semantic measures are proposed in (Fiorini et al, 2014).…”

Section: Other Applicationsmentioning

confidence: 99%

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Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

Self Cite

166

102

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Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

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Section: Knowledge Engineering Semantic Web and Linked Datamentioning

confidence: 99%

Section: Other Applicationsmentioning

confidence: 99%

Section: Other Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

Self Cite

166

102

View full text Add to dashboard Cite

show abstract

“…They described that the IR system depends on domain ontology to broaden the group of appropriate documents which are retrieved by employing graphical rending of query results to indulge user communications. In paper [3], authors presented method for semantic query in out-dated relational database by creating ontological layer. A schema ontology mined from relational database schema and modified with global domain ontology is wrapped.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Driven Information Retrieval for Healthcare Information System : A Case Study

Ibrahim¹,

Hashi²,

Mohamed³

2013

IJNSA

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In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing one single patient's information gathered from a large volume of data over a long period of time. The main objective of this paper is to represent our ontology-based information retrieval approach for clinical Information System. We have performed a Case Study in the real life hospital settings. The results obtained illustrate the feasibility of the proposed approach which significantly improved the information retrieval process on a large volume of data over a long period of time from August 2011 until January 2012

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“…Section 2 reviews different IR strategies and highlights theperformanceobtained using conceptual approaches, including OBIRS, an ontological based information retrieval system [55] which is more detailed as it serves as a basis for our hybrid IRS. Section 3 reviews the foundations of concept identification through lexical analysis and details the different phases of text segmentation that are implemented within the Synopsis approach [13].…”

Section: Fig 1 Overview Of Our Colexir Approachmentioning

confidence: 99%