Description Logic to Model a Domain Specific Information Retrieval System

Radhouani, Saïd; Falquet, Gilles; Chevalletinst, Jean-Pierre

doi:10.1007/978-3-540-85654-2_17

Cited by 5 publications

(3 citation statements)

References 10 publications

(7 reference statements)

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“…We also revealed that non-trivial queries, like those treated in our study, require adding domain knowledge during indexing and querying process. As shown in our previous work, this can be reached using expressive documents and queries languages, respectively during documents and queries content representation [16].…”

Section: Resultsmentioning

confidence: 95%

“…We notably showed how the use of semantic relationships could provide a precise representation of documents and query content. Relationships can therefore be used during the information retrieval process in order to allow the system to find a relevant document to a given query, even if it does not share any term with that query [16].…”

Section: Findings and Discussionmentioning

confidence: 99%

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Analysis of Word Sense Disambiguation-Based Information Retrieval

Guyot

Falquet

Radhouani

et al. 2009

Lecture Notes in Computer Science

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Abstract.Several studies have tried to improve retrieval performances based on automatic Word Sense Disambiguation techniques. So far, most attempts have failed. We try, through this paper, to give a deep analysis of the reasons behind these failures. During our participation at the Robust WSD task at CLEF 2008, we performed experiments on monolingual (English) and bilingual (Spanish to English) collections. Our official results and a deep analysis are described below, along with our conclusions and perspectives.

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Section: Resultsmentioning

confidence: 95%

Section: Findings and Discussionmentioning

confidence: 99%

Analysis of Word Sense Disambiguation-Based Information Retrieval

Guyot

Falquet

Radhouani

et al. 2009

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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“…One of the interests in using logic for IR is the fact that the results are predictable and explanatory. For example, Description Logic is explicitly used to formalize knowledge in medical IR systems [13,77]. Belief revision [54] is being investigated to cope with vague queries.…”

Section: Recent Logical Ir Systemsmentioning

confidence: 99%

Modeling Information Retrieval by Formal Logic

et al. 2019

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Several mathematical frameworks have been used to model the information retrieval (IR) process, among them, formal logics. Logic-based IR models upgrade the IR process from document-query comparison to an inference process, in which both documents and queries are expressed as sentences of the selected formal logic. The underlying formal logic also permits one to represent and integrate knowledge in the IR process. One of the main obstacles that has prevented the adoption and large-scale diffusion of logic-based IR systems is their complexity. However, several logic-based IR models have been recently proposed that are applicable to large-scale data collections. In this survey, we present an overview of the most prominent logical IR models that have been proposed in the literature. The considered logical models are categorized under different axes, which include the considered logics and the way in which uncertainty has been modeled, for example, degrees of belief or degrees of truth. Accordingly, the main contribution of the article is to categorize the state-of-theart logical models on a fine-grained basis, and for the considered models the related implementation aspects are described. Consequently, the proposed survey is finalized to better understand and compare the different logical IR models. Last, but not least, this article aims at reconsidering the potentials of logical approaches to IR by outlining the advances of logic-based approaches in close research areas.K. Abdulahhad et al. information need, formulated by a query. Based on this definition, an IRS has to deal with a collection of documents, with users' information needs, and with the notion of relevance.In IR, documents are carriers of information; in their original forms, they are humanunderstandable objects (e.g., Web pages, articles, books, and images), which an IRS must transform into machine-understandable objects. This process is called indexing, and its outcome is the association of a set of features (terms in textual documents) with documents. These features constitute the basic elements employed to formally represent a document. A user's information needs are motivated by a user's information gap; a query is a representation of these needs. Once formal representations have been provided for both documents and queries, the system compares them to assess the relevance of each document to the considered query.Relevance is a complex notion composed of several dimensions, such as topicality, popularity, and novelty, as has been well pointed out in the literature [33,65,95,96]. An IRS can only estimate relevance, and generally topicality is the core relevance dimension. The assessment of topical relevance 1 relies on the definition of a model, the IR model, which provides a formal means to represent and compare both documents and queries. In this survey we use the term retrieval status value (RSV) to indicate the numerical value produced by the estimate of topical relevance.Different mathematical theories have been employed to define IR models, w...

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Approximate matching-based unsupervised document indexing approach: application to biomedical domain

Boukhari

Omri

2020

Scientometrics

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Description Logic to Model a Domain Specific Information Retrieval System

Cited by 5 publications

References 10 publications

Analysis of Word Sense Disambiguation-Based Information Retrieval

Analysis of Word Sense Disambiguation-Based Information Retrieval

Modeling Information Retrieval by Formal Logic

Approximate matching-based unsupervised document indexing approach: application to biomedical domain

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