Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules

Buche, Patrice; Dervin, Catherine; Haemmerlé, Ollivier; Thomopoulos, Rallou

doi:10.1109/tfuzz.2004.841736

Cited by 36 publications

(23 citation statements)

References 25 publications

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“…And fuzzy web data tables are integrated and guided by using OTR. In these methods, symbolic concepts and numerical concepts are considered and the methods to instantiate concepts and relations, and querying the results are elucidated clearly [8]. In fuzzy web data tables integration we use ONDINE [1] system having web sub system and domain sub system.…”

Section: Related Workmentioning

confidence: 99%

Ontological Semi Automatic Method for Web Data Tables Integration

GowthamipriyaDarsini¹,

Hanmanthu²

2014

IJCA

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In this paper, we are presenting a model for implementation of ontology through invoking a semi-automatic process from web data tables. The singularity of this method is the implementation of cosine similarity for filtering the documents. We present the framework for the development of generic method concerning data integration. The data warehouse is composed of several data tables extracted from the web and it has been supplemented by the existing local data sources. Documents pertaining to different domains can be supplemented as input to the data sources which are stored in data ware house and then are converted into XML/RDF data. We have used cosine similarities to measure the similarity of two documents which are likely to be same in terms of their subjects. It is a semi automatic method to extract and integration of web documents by using ontology.

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Section: Related Workmentioning

confidence: 99%

Ontological Semi Automatic Method for Web Data Tables Integration

GowthamipriyaDarsini¹,

Hanmanthu²

2014

IJCA

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“…The basic idea of the Woogle method is the exploitation of the semantics of the identifiers chosen to describe WSDL elements. Buche [19] use multi-view fuzzy querying, in which fuzzy sets may be defined on a hierarchical domain of values called an ontology (values of the domain are connected using the a kind of semantic link), to query incomplete, imprecise and heterogeneously structured data stored in a relational database with fuzzy selection criteria.…”

Section: Related Workmentioning

confidence: 99%

An weighted ontology-based semantic similarity algorithm for web service

Liu

Shen

Hao

et al. 2009

Expert Systems with Applications

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/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1016/j.eswa. 2009.04.034 International Journal of Expert Systems and Applications, 36, 10, pp. 12480-12490, 2009-12-01 A weighted ontology-based semantic similarity algorithm for web services Liu, M.; Shen, W.; Hao, Q.; Yan, J. The material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. For more information visit http://laws.justice.gc.ca/en/showtdm/cs/C-42Les renseignements dans ce document sont protégés par la Loi sur le droit d'auteur, par les lois, les politiques et les règlements du Canada et des accords internationaux. Ces dispositions permettent d'identifier la source de l'information et, dans certains cas, d'interdire la copie de documents sans permission écrite. Pour obtenir de plus amples renseignements : http://lois.justice.gc.ca/fr/showtdm/cs/C-42 1 A weighted ontology-based semantic similarity algorithm for web service

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“…Tolerant fuzzy pattern matching (Dubois & Prade, 1995) uses a similarity relation between elements to enlarge the preferences, but it does not take into account the case of hierarchically organized domains. For instance, elements may be added to the support of the fuzzy set in the enlargement mechanism, but more specific elements than those may remain outside of it, which is a major drawback for hierarchical domains (see Buche et al 2005 for more details). In this section, more than a unique solution, we propose a methodology in order to generalize a hierarchical fuzzy set expressing preferences.…”

Section: Generalization Of a Hfsmentioning

confidence: 99%

Hierarchical Fuzzy Sets to Query Possibilistic Databases

Thomopoulos

Buche

Haemmerlé

2008

Handbook of Research on Fuzzy Information Processing in Databases

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Within the framework of flexible querying of possibilistic databases, based on the fuzzy set theory, this chapter focuses on the case where the vocabulary used both in the querying language and in the data is hierarchically organized, which occurs in systems that use ontologies. We give an overview of previous works concerning two issues: firstly, flexible querying of imprecise data in the relational model; secondly, the introduction of fuzziness in hierarchies. Concerning the latter point, we develop an aspect where there is a lack of study in current literature: fuzzy sets whose definition domains are hierarchies. Hence we propose the concept of hierarchical fuzzy set and present its properties. We present its application in the MIEL flexible querying system, for the querying of two imprecise relational databases, including user interfaces and experimental results.

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Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules

Cited by 36 publications

References 25 publications

Ontological Semi Automatic Method for Web Data Tables Integration

Ontological Semi Automatic Method for Web Data Tables Integration

An weighted ontology-based semantic similarity algorithm for web service

Hierarchical Fuzzy Sets to Query Possibilistic Databases

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