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.

Community-Driven Ontology Matching

Abstract: We extend the notion of ontology matching to community-driven ontology matching. Primarily, the idea is to enable Web communities to establish and reuse ontology mappings in order to achieve, within those communities, an adequate and timely domain representation, facilitated knowledge exchange, etc. Secondarily, the matching community is provided with the new practice, which is a public alignment reuse. Specifically, we present an approach to construction of a community-driven ontology matching system and discuss its implementation. An analysis of the system usage indicates that our strategy is promising. In particular, the results obtained justify feasibility and usefulness of the community-driven ontology mappings' acquisition and sharing.

Automatic generation of ontology for scholarly semantic web

Abstract: Semantic Web provides a knowledge-based environment that enables information to be shared and retrieved effectively In this research, we propose the Scholarly Semantic Web for the sharing, reuse and management of scholarly information To support the Scholarly Semantic Web, we need to construct ontology from data which is a tedious and difficult task To generate ontology automatically, Formal Concept Analysis (FCA) is an effective technique that can formally abstract data as conceptual structures To enable FCA to deal with uncertainty in data and interpret the concept hierarchy reasonably, we propose to incorporate fuzzy logic into FCA for automatic generation of ontology The proposed new framework is known as Fuzzy Formal Concept Analysis (FFCA) In this paper, we will discuss the Scholarly Semantic Web, and the ontology generation process from the FFCA framework In addition, the performance of the FFCA framework for ontology generation will also be evaluated and presented

CAD/CAPP Integration using Feature Ontology

Abstract: In a collaborative computer-supported engineering environment, the interoperation of various applications will need a representation that goes beyond the current geometry-based representation, which is inadequate for capturing semantic information. The primary purpose of this study is to discuss a semantically based information exchange protocol that will facilitate seamless interoperability among current and next generation computer-aided design systems (CAD) and between CAD and other systems that use product data. An ontological approach is described to integrating computer-aided design (CAD) and computer-aided process planning (CAPP). Two commercial software applications are used to demonstrate the approach. This involves the development of a shared ontology and domain specific ontologies in the Knowledge Interchange Format (KIF) language. Domain specific ontologies — which are feature-based — are developed after a detailed analysis of the CAD and the CAPP software. Mapping between the domain ontologies and the shared ontology is achieved by several mapping rules. The approach is validated by using a variety of parts.

Ontology-based database access

Abstract: One of the most interesting usages of shared conceptualizations is ontology-based data access. That is, to the usual data layer of an information system we superimpose a conceptual layer to be exported to the client. Such a layer allows the client to have a conceptual view of the information in the system, which abstracts away from how such information is maintained in the data layer of the system itself. While ontologies are the best candidates for realizing the conceptual layer, relational DBMSs are natural candidates for the management of the data layer. The need of efficiently processing large amounts of data requires ontologies to be expressed in a suitable fragment of OWL: the fragment should allow, on the one hand, for modeling the kind of intensional knowledge needed in real-world applications, and, on the other hand, for delegating to a relational DBMS the part of reasoning (in particular query answering) that deals with the data. In this paper, we propose one such a fragment, in fact the largest fragment currently known to satisfy the above requirements. Furthermore, we provide means to access databases that are independent from the ontology, by proposing a novel mapping language that solves the so-called “impedance mismatch” between values in the databases and objects represented in the ontology.

LOM: A Lexicon-based Ontology Mapping Tool

Abstract: : Ontology mapping is important to knowledge sharing and semantic integration but hard to completely automate. LOM is a semi-automatic lexicon-based ontology-mapping tool that supports a human mapping engineer with a first-cut comparison of ontological terms between the ontologies to be mapped, based on their lexical similarity. This paper will explain the algorithms used, the tests performed, and the applications developed using the results of this approach. It will also discuss the limitations of this approach as well as the future research and development issues in this field.