An ontological approach to federated data integration

During the last years a lot of projects and lines of research have emerged from different proposals trying to find the best way to reach data integration. Two powerful techniques have appeared separately – ontology and contextual information – in order to help solve semantic heterogeneity problems. In our proposal we combine both techniques exploiting the advantages of each of them. We propose a new approach, in which three main components work together in order to achieve a consistent integration. Each component contains some type of semantic information modeled by ontologies and contexts. Our approach helps the building of each of the components and address other types of heterogeneity such as ontological heterogeneity.Eje: Ingeniería de Software y Bases de Datos (ISBD)Red de Universidades con Carreras en Informática (RedUNCI

An ontological approach to federated data integration

During the last years a lot of projects and lines of research have emerged from different proposals trying to find the best way to reach data integration. Two powerful techniques have appeared separately – ontology and contextual information – in order to help solve semantic heterogeneity problems. In our proposal we combine both techniques exploiting the advantages of each of them. We propose a new approach, in which three main components work together in order to achieve a consistent integration. Each component contains some type of semantic information modeled by ontologies and contexts. Our approach helps the building of each of the components and address other types of heterogeneity such as ontological heterogeneity.Eje: Ingeniería de Software y Bases de Datos (ISBD)Red de Universidades con Carreras en Informática (RedUNCI

An ontological approach to federated data integration

During the last years a lot of projects and lines of research have emerged from different proposals trying to find the best way to reach data integration. Two powerful techniques have appeared separately – ontology and contextual information – in order to help solve semantic heterogeneity problems. In our proposal we combine both techniques exploiting the advantages of each of them. We propose a new approach, in which three main components work together in order to achieve a consistent integration. Each component contains some type of semantic information modeled by ontologies and contexts. Our approach helps the building of each of the components and address other types of heterogeneity such as ontological heterogeneity.Eje: Ingeniería de Software y Bases de Datos (ISBD)Red de Universidades con Carreras en Informática (RedUNCI

Spatial location and its relevance for terminological inferences in bio-ontologies

<p>Abstract</p> <p>Background</p> <p>An adequate and expressive ontological representation of biological organisms and their parts requires formal reasoning mechanisms for their relations of physical aggregation and containment.</p> <p>Results</p> <p>We demonstrate that the proposed formalism allows to deal consistently with "role propagation along non-taxonomic hierarchies", a problem which had repeatedly been identified as an intricate reasoning problem in biomedical ontologies.</p> <p>Conclusion</p> <p>The proposed approach seems to be suitable for the redesign of compositional hierarchies in (bio)medical terminology systems which are embedded into the framework of the OBO (Open Biological Ontologies) Relation Ontology and are using knowledge representation languages developed by the Semantic Web community.</p

Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers

Ontology-based data integration methods: a framework for comparison

A data integration system provides a uniform interface to distributed and heterogeneous sources. These sources can be databases as well as unstructured information such as files, HTML pages, etc. One of the most important problems within data integration is the semantic heterogeneity, which analyzes the meaning of terms included in the different information sources. This survey describes seven systems and three proposals for ontology -based data integration. An important feature is that all of them use, in some way, ontologies as the way to solve problems about semantic heterogeneity. In this paper, we show similarities and differences among the systems by providing a framework for comparison and classification.Keywords: Data Integration, Ontology, Semantic Heterogeneity