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.

Representing the UMLS® Semantic Network using OWL: (Or what's in a Semantic Web link?)

Abstract: The Semantic Network, a component of the Unified Medical Language Systems (UMLS), describes core biomedical knowledge consisting of semantic types and relationships. It is a well established, semi-formal ontology in widespread use for over a decade. We expected to publish this ontology on the Semantic Web, using OWL, with relatively little effort. However, we ran into a number of problems concerning alternative interpretations of the SN notation and the inability to express some of the interpretations in OWL. We detail these problems, as a cautionary tale to others planning to publish pre-existing ontologies on the Semantic Web, as a list of issues to consider when describing formally concepts in any ontology, and as a collection of criteria for evaluating alternative representations, which could form part of a methodology of ontology development.

Ontology Engineering with Ontology Design Patterns: Foundations and Applications

Abstract: The use of ontologies for data and knowledge organization has become ubiquitousin many dataintensive and knowledgedriven application areas, in science, industry,and the humanities. At the same time, ontology engineering best practices continueto evolve. In particular, modular ontology modeling based on ontology designpatterns is establishing itself as an approach for creating versatile and extendableontologies for data management and integration. This book is the very first comprehensive treatment of Ontology Engineering withOntology Design Patterns. It contains both advanced and introductory materialaccessible for readers with only a minimal background in ontology modeling. Someintroductory material is written in the style of tutorials, and specific chapters aredevoted to examples and to applications. Other chapters convey the state of the artin research regarding ontology design patterns. The editors and the contributing authors include the leading contributors to thedevelopment of ontologydesignpatterndriven ontology engineering.

Enriching ontology mappings with semantic relations

Abstract: There is a large number of tools to match or align corresponding concepts between ontologies. Most tools are restricted to equality correspondences, although many concepts may be related differently, e.g. according to an is-a or part-of relationship. Supporting such additional semantic correspondences can greatly improve the expressiveness of ontology mappings and their usefulness for tasks such as ontology merging and ontology evolution. We present a new approach called STROMA (SemanTic Refinement of Ontology MAppings) to determine semantic ontology mappings. In contrast to previous approaches, it follows a so-called enrichment strategy that refines the mappings determined with a state-of-the-art match tool. The enrichment strategy employs several techniques including the use of background knowledge and linguistic approaches to identify the additional kinds of correspondences. We evaluate the approach in detail using several real-life benchmark tests. A comparison with different tools for semantic ontology matching confirms the viability of the proposed enrichment strategy.

KaBOB: ontology-based semantic integration of biomedical databases.

Abstract: The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources. We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license. KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

Ontology-based integration for relational databases

Abstract: In this paper, we show that representation and reasoning techniques used in traditional knowledge engineering and the emerging Semantic Web can play an important role for heterogeneous database integration. Our OntoGrate architecture combines ontology-based schema representation, first order logic inference, and some SQL wrappers to integrate two sample relational databases. We define inferential data integration as the theoretical framework for our approach. The performance evaluation for query answering shows that OntoGrate reformulates conjunctive queries and retrieves over 100,000 answers from a target database in under 30 seconds. In addition to query answering, the system translates 40,000 database facts from source to target in under 30 seconds.