Literature-based discovery on the World Wide Web

Gordon, Michael D.; Lindsay, Robert K.; Fan, Weiguo

doi:10.1145/604596.604597

Cited by 42 publications

(43 citation statements)

References 16 publications

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“…2.1. Etat de l'art : Gordon et Lindsay, qui avaient simulé les expériences de Swanson ont travaillé sur l'utilisation d'Internet dans un système de découverte de connaissances [Gordon, 2002]. Ils proposent de généraliser le modèle utilisé dans le domaine biomédical en un modèle ouvert pour la découverte de connaissances au sens large.…”

Section: Transposition De La Découverte De Connaissances Au Monde Du unclassified

Détecter l’innovant sur le web par des techniques non booléennes : méthode, outils, application

2013

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Détecter l'innovant sur le web par des techniques non booléennes : méthode, outils, application Résumé : La problématique de l'ident ification de signaux faibles est centrale en Intelligence économique. Le web, par son caractère informel, apparaît comme une source d'information à privilégier. Le problème qui se pose alors est celui de la collecte et du traitement d'une information massive peu structurée. Cette communication présente une approche non booléenne hybride de révélation de connaissances innovantes combinant une recherche informelle sur le web et une recherche très structurée dans un thésaurus. Abstract:The problem of the id entification of weak signals is central in competitive Intelligence.The Web seems to be a very appropriate information source to detect such signals. The difficulty is the collection and the treatment of a mass and little structured data. This communication presents a hybrid non-Boolean approach that reveals innovating knowledge. The approach used information coming from the internet and information collected through a thesaurus.

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Section: Transposition De La Découverte De Connaissances Au Monde Du unclassified

Détecter l’innovant sur le web par des techniques non booléennes : méthode, outils, application

2013

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“…For example, Gordon and Lindsay (1996, 1998, 2001 used tf * idf to extract concepts from texts. Moreover, UMLS and MeSH are widely used as the semantic tool to identify concepts.…”

Section: Related Researchmentioning

confidence: 99%

A compound correlation model for disjoint literature‐based knowledge discovery

Huang

He²,

Yang³

et al. 2012

Aslib Proceedings

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Purpose -The algorithm of disjoint literature-based knowledge discovery provides a convenient, efficient and effective auxiliary method for scientific research. Based on an analysis of Swanson's A-B-C model of disjoint literature-based knowledge discovery and Gordon's intermediate literature theory, this paper seeks to propose a more comprehensive compound correlation model for disjoint literature-based knowledge discovery. Design/methodology/approach -A new algorithm of vector space model (VSM) based disjoint literature-based knowledge discovery is designed to implement the compound correlation model. Findings -The validity tests showed that this new model not only simulated both of Swanson's early and well-known discoveries of Raynaud's disease-fish oil and migraine-magnesium connections successfully, but also applied to knowledge discovery in the agricultural economics literature in the Chinese language. Research limitations/implications -Although the workload was reduced to the minimum under the compound correlation model compared with other algorithms and models, part of the work needed some manual intervention in the process of disjoint literature-based knowledge discovery with the VSM-based compound correlation model. Practical implications -The algorithm was capable of knowledge discovery with a large-scale dataset and had an advantage in identifying a series of hidden connections among a set of literatures. Therefore, application of the model might be extended to more fields. Originality/value -Traditional two-step knowledge discovery procedures were integrated into the model, which contained open and closed disjoint literature-based knowledge discovery.

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“…A related field called Literature-based discovery (LBD) concerns methods for identifying unknown relationships in data drawn from published results [5]. By bringing together results published in several papers, new relationships that were not considered in the original studies can be found.…”

Section: Related Researchmentioning

confidence: 99%

Using Published Medical Results and Non-homogenous Data in Rule Learning

Wojtusiak

Irvin

Birerdinc

et al. 2011

2011 10th International Conference on Machine Learning and Applications and Workshops

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Many factors limit researchers from accessing studies' original data sets. As a result, much medical and healthcare research is based off of systematic reviews and meta-analysis of published results. However, when research involves the use of aggregated data from multiple studies, traditional machine learning-based means of analysis cannot be used. This paper describes diversity of data and results available in published manuscripts, and relates them to a rule learning method that can be applied to build classification and predictive models from such input. The method can be used to support meta-analysis and systematic reviews. Two application areas are used to illustrate the discussed issues: diagnosis of liver diseases in patients with metabolic syndrome, and detection of polycystic ovary syndrome.

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Literature-based discovery on the World Wide Web

Cited by 42 publications

References 16 publications

Détecter l’innovant sur le web par des techniques non booléennes : méthode, outils, application

Détecter l’innovant sur le web par des techniques non booléennes : méthode, outils, application

A compound correlation model for disjoint literature‐based knowledge discovery

Using Published Medical Results and Non-homogenous Data in Rule Learning

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