scispace - formally typeset
Search or ask a question
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.


Papers
More filters
Proceedings ArticleDOI
08 Jul 2003
TL;DR: A methodological approach and a flexible environment for ontology management that enables the building of extensible ontologies, and the mappingfiom ontologies to information sources is proposed.
Abstract: Ontologies have received increasing interest in the computer science community and their benefits have been recognized in many areas. In this paper, we discuss the role of ontologies to facilitate information fusion fiom heterogeneous data and knowledge sources in support of high-level information fusion processes. We review several approaches where ontologies help provide semantic integration of information. We present preliminary work about ontological engineering for level 2 and 3 information fusion that should help semantic integration. Ontology development methods and tools should support the ontological engineering process. To this end, we propose a methodological approach and a flexible environment for ontology management that enables the building of extensible ontologies, and the mappingfiom ontologies to information sources.

97 citations

20 Oct 2003
TL;DR: A similarity measure is introduced that takes advantage of most of the features of OWL-Lite ontologies and integrates many ontology comparison techniques in a common framework and put forth a computation technique to deal with one-to-many relations and circularities in the similarity definitions.
Abstract: Integrating heterogeneous resources of the web will require finding agreement between the underlying ontologies. A variety of methods from the literature may be used for this task, basically they perform pair-wise comparison of entities from each of the ontologies and select the most similar pairs. We introduce a similarity measure that takes advantage of most of the features of OWL-Lite ontologies and integrates many ontology comparison techniques in a common framework. Moreover, we put forth a computation technique to deal with one-to-many relations and circularities in the similarity definitions.

97 citations

01 Jan 1999
TL;DR: SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment, is developed, based on an extremely general knowledge model and, therefore, can be applied across various platforms.
Abstract: As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user’s actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms. 1 Merging Versus Alignment In recent years, researchers have developed many ontologies. These different groups of researchers are now beginning to work with one another, so they must bring together these disparate source ontologies. Two approaches are possible: (1) merging the ontologies to create a single coherent ontology, or (2) aligning the ontologies by establishing links between them and allowing them to reuse information from one another. As an illustration of the possible processes that establish correspondence between different ontologies, we consider the ontologies that natural languages embody. A researcher trying to find common ground between two such languages may perform one of several tasks. He may create a mapping between the two languages to be used in, say, a machine-translation system. Differences in the ontologies underlying the two languages often do not allow simple one-to-one correspondence, so a mapping must account for these differences. Alternatively, Esperanto language (an international language that was constructed from words in different European languages) was created through merging: All the languages and their underlying ontologies were combined to create a single language. Aligning languages (ontologies) is a third task. Consider how we learn a new domain language that has an extensive vocabulary, such as the language of medicine. The new ontology (the vocabulary of the medical domain) needs to be linked in our minds to the knowledge that we already have (our existing ontology of the world). The creation of these links is alignment. We consider merging and alignment in this paper. For simplicity, throughout the discussion, we assume that only two ontologies are being merged or aligned at any given time. Figure 1 illustrates the difference between ontology merging and alignment. In merging, a single ontology that is a merged version of the original ontologies is created. Often, the original ontologies cover similar or overlapping domains. For example, the Unified Medical Language System (Humphreys and Lindberg 1993; UMLS 1999) is a large merged ontology that reconciles differences in terminology from various machine-readable biomedical information sources. Another example is the project that was merging the top-most levels of two general commonsense-knowledge ontologies—SENSUS (Knight and Luk 1994) and Cyc (Lenat 1995)—to create a single top-level ontology of world knowledge (Hovy 1997). In alignment, the two original ontologies persist, with links established between them. Alignment usually is performed when the ontologies cover domains that are complementary to each other. For example, part of the High Performance Knowledge Base (HPKB) program sponsored by the Defense Advanced Research Projects Agency (DARPA) (Cohen et al. 1999) is structured around one central ontology, the Cyc knowledge base (Lenat 1995). Several teams of researchers develop ontologies in the domain of military tactics to cover the types of military units and weapons, tasks the units can perform, constraints on the units and tasks, and so on. These developers then align these more domain-specific ontologies to Cyc by establishing links into Cyc’c upperand middle-level ontologies. The domain-specific ontologies do not become part of the Cyc knowledge base; rather, they are separate ontologies that include Cyc and use its top-level distinctions. 1 Most knowledge representation systems would require one ontology to be included in the other for the links to be established.

96 citations

Book ChapterDOI
29 Oct 2006
TL;DR: It is concluded that ontology engineering research should strive for a unified, lightweight and component-based methodological framework, principally targeted at domain experts, in addition to consolidating the existing approaches.
Abstract: The theoretical results achieved in the ontology engineering field in the last fifteen years are of incontestable value for the prospected large scale take-up of semantic technologies Their range of application in real-world projects is, however, so far comparatively limited, despite the growing number of ontologies online available This restricted impact was confirmed in a three month empirical study, in which we examined over 34 contemporary ontology development projects from a process- and costs-oriented perspective In this paper we give an account of the results of this study We conclude that ontology engineering research should strive for a unified, lightweight and component-based methodological framework, principally targeted at domain experts, in addition to consolidating the existing approaches

96 citations

Proceedings Article
11 Jul 2010
TL;DR: A novel probabilistic-logical framework for ontology matching based on Markov logic is presented that has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values.
Abstract: Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.

95 citations


Network Information
Related Topics (5)
Server
79.5K papers, 1.4M citations
84% related
Graph (abstract data type)
69.9K papers, 1.2M citations
84% related
Software development
73.8K papers, 1.4M citations
84% related
User interface
85.4K papers, 1.7M citations
84% related
Support vector machine
73.6K papers, 1.7M citations
83% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
2022149
202111
202011
201919
201843