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.

Ontology Learning Part One — on Discovering Taxonomic Relations from the Web

Abstract: Ontologies may help to facilitate the finding and use of Web information However, the engineering of an ontology may turn out to be expensive and time-consuming Therefore, we exploit ontology learning techniques that automate ontology engineering to some extent In this chapter, we focus on the learning of the taxonomic backbone of ontologies, presenting a survey on algorithms as well as on some new ideas that consider the structure of existing ontology parts Eventually, we describe an evaluation of our proposal and give concrete results

Multidimensional integrated ontologies: a framework for designing semantic data warehouses

Abstract: The Semantic Web enables organizations to attach semantic annotations taken from domain and application ontologies to the information they generate. The concepts in these ontologies could describe the facts, dimensions and categories implied in the analysis subjects of a data warehouse. In this paper we propose the Semantic Data Warehouse to be a repository of ontologies and semantically annotated data resources. We also propose an ontology-driven framework to design multidimensional analysis models for Semantic Data Warehouses. This framework provides means for building a Multidimensional Integrated Ontology (MIO) including the classes, relationships and instances that represent interesting analysis dimensions, and it can be also used to check the properties required by current multidimensional databases (e.g., dimension orthogonality, category satisfiability, etc.) In this paper we also sketch how the instance data of a MIO can be translated into OLAP cubes for analysis purposes. Finally, some implementation issues of the overall framework are discussed.

Evaluation of ontology merging tools in bioinformatics.

Abstract: Ontologies are being used nowadays in many areas, including bioinformatics. One of the issues in ontology research is the aligning and merging of ontologies. Tools have been developed for ontology merging, but they have not been evaluated for their use in bioinformatics. In this paper we evaluate two of the most well-known ontology merging tools with a bioinformatics perspective. As test ontologies we have used Gene Ontology and Signal-Ontology.

Preface: tutorial on ontological engineering

Abstract: PREFACE This tutorial course describes the current state of the art of ontological engineering which is a successor of knowledge engineering. It covers theory, tools and applications and consists of three parts: Part 1 is an introduction to ontological engineering, Part 2 describes ontology development, languages and tools, and Part 3 is an advanced course dealing with philosophical issues of ontology design together with detailed guidelines of ontology development. Part 3 also presents a success story of ontological engineering with the deployment result in a company. The philosophy behind this tutorial is that ontological engineering is viewed as a challenge to enabling knowledge sharing and reuse which knowledge engineering failed to realize. Therefore, one of the major topics dealt with in this tutorial is to explain what an ontology should be while explaining how it is understood currently.

Design of product ontology architecture for collaborative enterprises

Abstract: As enterprises are subject to cope with frequently changing business environment, enterprises should integrate value chains such as supply chain and design chain. Sharing product information must precede for the integration. However, because most of the participants have different business experience and business domains, interoperability of product information among enterprises should be guaranteed for collaboration. To achieve interoperability, we suggest product ontology architecture through the investigation of generic ontology architecture. We first suggest 4-layered ontology architecture for an integrated value chain. Extending this ontology architecture, we develop product ontology architecture which facilitates building product ontologies that are referred to all related participants inbound and outbound of the enterprise for collaboration. Using a product ontology, each enterprise can have semantic interoperability across each other for collaborative works. Because product ontologies have the feature of evolving through product lifecycle, the proposed product ontology architecture reflects this evolving feature to guarantee semantic interoperability.