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.

A public transportation ontology to support user travel planning

Abstract: Choose the best way to move from one place to another can involve different information: offers of different transport modes, their combination in the same journey and other information about services (such as restaurants, libraries, etc) that can be available in the route and useful for the passenger. Different approaches have been proposed to support the passenger's planning considering some part of this information. In this paper we present a public transportation domain ontology that considers different concepts related to the best and more relevant planning for the passenger. This ontology is formalized with OWL in Protege tool. Using real instances and inferences, we show the ontology application, its relevance and consistency.

Ontology-based information integration in the automotive industry

Abstract: Information integration is still one of today's hottest IT topics. Neither merging the information from different data sources nor preparing it for the end user's access has been completely solved. The goal of this paper is to present a holistic approach to integration by using ontologies and logic. There are several reasons for an ontology-based approach: Ontologies are able to cover all occurring data structures, for ontologies can be seen as nowadays most advanced knowledge representation model. They are able to cover complexity, for the combination with deductive logic extends the mapping and business logic capabilities. As the model is separated from the data storage, we get a higher degree of abstraction, whereby the semantics of the whole system is increased. Ontologies are extendible and highly reusable and deliver the user a better access to his relevant content. In this paper we describe how current capabilities of knowledge representation, mapping of structures and description of the business logic are extended. In an elaborated case study of a specific R&D process in the automobile industry [Ste03] we demonstrate that the complex integration process can be realized much easier, faster, more understandable and less expensive than before, without changing the existing IT legacy environment.

Introduction to the Ontology Alignment Evaluation 2005

Abstract: The increasing number of methods available for schema matching/ontology integration suggests the need to establish a consensus for evaluation of these methods. The Ontology Alignment Evaluation Initiative1 is now a coordinated international initiative that has been set up for organising evaluation of ontology matching algorithms. After the two events organized in 2004 (namely, the Information Interpretation and Integration Conference (I3CON) and the EON Ontology Alignment Contest [4]), this year one unique evaluation campaign is organised. Its outcome is presented at the Workshop on Integrating Ontologies held in conjunction with K-CAP 2005 at Banff (Canada) on October 2, 2005. Since last year, we have set up a web site, improved the software on which the tests can be evaluated and set up some precise guidelines for running these tests. We have taken into account last year’s remarks by (1) adding more coverage to the benchmarck suite and (2) elaborating two real world test cases (as well as addressing other technical comments). This paper serves as a presentation to the 2005 evaluation campaign and introduction to the results provided in the following papers.

Using ontologies to improve semantic interoperability in health data

Abstract: The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual person-based records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.