Topic
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.
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24 Oct 2006
TL;DR: The research examines the concepts and their hierarchy in conceptual model, the common feature of the most ontologies, which reflects the fundamental complexity, and suggests a well-defined metrics suite of complexity, which mainly examine the quantity, ratio and correlativity of concepts and relationships.
Abstract: With the tremendous development in size, the complexity of ontology increases. Thus ontology evaluation becomes extremely important for developers to determine the fundamental characteristics of ontologies in order to improve the quality, estimate cost and reduce future maintenance. Our research examines the concepts and their hierarchy in conceptual model, the common feature of the most ontologies, which reflects the fundamental complexity. We suggest a well-defined metrics suite of complexity, which mainly examine the quantity, ratio and correlativity of concepts and relationships, to evaluate ontologies from the viewpoint of complexity and its evolution. In the study, we measure three ontologies in GO to verify our metrics. The results indicate that this metrics suite works well, and the biological process ontology is the most complex one from the view of complexity, and the molecular function ontology is the unsteadiest one from the view of evolution.
62 citations
TL;DR: This paper initiates a framework for assessing the informational value of data that includes data dimensions; aligning data quality with business practices; identifying authoritative sources and integration keys; merging models; uniting updates of varying frequency and overlapping or gapped data sets.
62 citations
03 Nov 2008
TL;DR: This paper discusses how the two worlds can be brought together by combining the high-level axiomatizations from the standard upper merged ontology (SUMO) with the extensive world knowledge of the YAGO ontology, resulting in a new large-scale formal ontology.
Abstract: Ontologies are becoming more and more popular as background knowledge for intelligent applications. Up to now, there has been a schism between manually assembled, highly axiomatic ontologies and large, automatically constructed knowledge bases. This paper discusses how the two worlds can be brought together by combining the high-level axiomatizations from the standard upper merged ontology (SUMO) with the extensive world knowledge of the YAGO ontology. The result is a new large-scale formal ontology, which provides information about millions of entities such as people, cities, organizations, and companies.
62 citations
TL;DR: A Genetic Fuzzy Agent using the ontology model for Meeting Scheduling System (MSS) is presented in this paper and the experimental results show that the approach can effectively work for MSS.
62 citations
29 Mar 2004
TL;DR: This paper proposes an approach for distributed ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology, knowing that this approach will play an important role in improving the efficiency of information retrieval.
Abstract: The new era of semantic Web has enabled users to extract semantically relevant data from the Web. The backbone of the semantic Web is a shared uniform structure which defines how Web information is split up regardless of the implementation language or the syntax used to represent the data. This structure is known as an ontology. As information on the Web increases significantly in size, Web ontologies also tend to grow bigger, to such an extent that they become too large to be used in their entirety by any single application. This has stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology. Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. Because of the size of the original ontology, the process of repeatedly iterating the millions of nodes and relationships to form an optimized sub-ontology can be very extensive. Therefore we have identified the need for a distributed approach to the extraction process. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of information retrieval.
61 citations