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Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.


Papers
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01 Jan 2008
TL;DR: This paper provides a discussion on existing ontology learning techniques and the state of the art of the field.
Abstract: Ontologies constitute an approach for knowledge representation that can be shared establishing a shared vocabulary for different applications and are also the backbone of the Semantic Web. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. However, ontology development is a difficult and time consuming task. Ontology learning is an approach for the problem of knowledge acquisition bottleneck that aims at reducing the cost of ontology construction through the development of automatic methods for the extraction of knowledge about a specific domain and its representation in an ontology like structure. This paper provides a discussion on existing ontology learning techniques and the state of the art of the field.

71 citations

Journal ArticleDOI
TL;DR: Using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented and the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

71 citations

Journal ArticleDOI
TL;DR: In this paper, the ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL).
Abstract: Background Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning.

71 citations

01 Jan 2006
TL;DR: The authors' on-going research on modelling uncertainty in ontologies based on Bayesian networks (BN) includes extending OWL to allow additional probabilistic markups for attaching probability information and converting a probabilistically annotated OWL ontology into a BN structure by a set of structural translation rules.
Abstract: : Dealing with uncertainty is crucial in ontology engineering tasks such as domain modelling, ontology reasoning, and concept mapping between ontologies. This paper presents the authors' on-going research on modelling uncertainty in ontologies based on Bayesian networks (BN). The work includes the following: (1) extending OWL to allow additional probabilistic markups for attaching probability information, (2) directly converting a probabilistically annotated OWL ontology into a BN structure by a set of structural translation rules, and (3) constructing the conditional probability tables (CPTs) of this BN using a new method based on iterative proportional fitting procedure (IPFP). The translated BN can support more accurate ontology reasoning under uncertainty as Bayesian inferences.

70 citations

Journal ArticleDOI
01 Jul 2006
TL;DR: A heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems are presented.
Abstract: In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported.

70 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
2022149
202111
202011
201919
201843