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.

Why evaluate ontology technologies? Because it works!

Abstract: We deal with two types of ontology evaluation, content evaluation and ontology technology evaluation. Evaluating content is a must for preventing applications from using inconsistent, incorrect, or redundant ontologies. It's unwise to publish an ontology that one or more software applications will use without first evaluating it. A well-evaluated ontology won't guarantee the absence of problems, but it makes its use safer. Similarly, evaluating ontology technology eases its integration with other software environments, ensuring a correct technology transfer from the academic to the industrial world. We also discuss ontology libraries, ontology tool, and formal evaluation of ontology quality.

Logical Development of the Cell Ontology

Abstract: The Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL. Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types. Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.

Products and Services Ontologies: A Methodology for Deriving OWL Ontologies from Industrial Categorization Standards

Abstract: Using Semantic Web technologies for e-business tasks, like product search or content integration, requires ontologies for products and services. Their manual creation is problematic due to (1) the high specificity, resulting in a large number of concepts, and (2) the need for timely ontology maintenance due to product innovation; and due to cost, since building such ontologies from scratch requires significant resources. At the same time, industrial categorization standards, like UNSPSC, eCl@ss, eOTD, or the RosettaNet Technical Dictionary, reflect some degree of consensus and contain a wealth of concept definitions plus a hierarchy. They can thus be valuable input for creating domain ontologies. However, the transformation of existing standards, originally developed for some purpose other than ontology engineering, into useful ontologies is not as straightforward as it appears. In this paper, (1) we argue that deriving products and services ontologies from industrial taxonomies is more feasible than manual ontology engineering; (2) show that the representation of the original semantics of the input standard, especially the taxonomic relationship, is an important modeling decision that determines the usefulness of the resulting ontology; (3) illustrate the problem by analyzing existing ontologies derived from UNSPCS and eCl@ss; (4) present a methodology for creating ontologies in OWL based on the reuse of existing standards; and (5) demonstrate this approach by transforming eCl@ss 5.1 into a practically useful products and services ontology.

Building Ontologies at the Knowledge Level using the Ontology Design Environment

Abstract: This paper discusses how ontologies can be specified at the knowledge level using the set of intermediate representations (Gomez-Perez, Fernandez & de Vicente 1996) proposed by METHONTOLOGY (Fernandez, Gomez-Perez & Juristo 1997; and Gomez-Perez 1998). These intermediate representations bridge the gap between how people think about a domain and the languages in which ontologies are formalized. Thus, METHONTOLOGY enables experts and ontology makers unfamiliar with implementation environments to build ontologies from scratch. In this paper, we also present the ODE (Ontology Design Environment) as a software tool to specify ontologies at the knowledge level. ODE allows developers to specify their ontology by filling in tables and drawing graphs. Its multilingual generator module automatically translates the specification of the ontology into target languages.

Ontology module extraction for ontology reuse: an ontology engineering perspective

Abstract: Problems resulting from the management of shared, distributed knowledge has led to ontologies being employed as a solution, in order to effectively integrate information across applications. This is dependent on having ways to share and reuse existing ontologies; with the increased availability of ontologies on the web, some of which include thousands of concepts, novel and more efficient methods for reuse are being devised. One possible way to achieve efficient ontology reuse is through the process of ontology module extraction. A novel approach to ontology module extraction is presented that aims to achieve more efficient reuse of very large ontologies; the motivation is drawn from an Ontology Engineering perspective. This paper provides a definition of ontology modules from the reuse perspective and an approach to module extraction based on such a definition. An abstract graph model for module extraction has been defined, along with a module extraction algorithm. The novel contribution of this paper is a module extraction algorithm that is independent of the language in which the ontology is expressed. This has been implemented in ModTool; a tool that produces ontology modules via extraction. Experiments were conducted to compare ModTool to other modularisation methods.