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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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Proceedings Article
01 Jan 2004
TL;DR: A new algorithm for matching two ontologies based on all the information available about the given ontologies (e.g. their concepts, relations, information about the structure of each hierarchy of concepts, or of relations) is proposed.
Abstract: Ontologies are nowadays used in many domains such as Semantic Web, information systems... to represent meaning of data and data sources. In the framework of knowledge management in an heterogeneous organization, the materialization of the organizational memory in a “corporate semantic web” may require to integrate the various ontologies of the different groups of this organization. To be able to build a corporate semantic web in an heterogeneous, multi-communities organization, it is essential to have methods for comparing, aligning, integrating or mapping different ontologies. This paper proposes a new algorithm for matching two ontologies based on all the information available about the given ontologies (e.g. their concepts, relations, information about the structure of each hierarchy of concepts, or of relations), applying TF/IDF scheme (a method widely used in the information retrieval community) and integrating WordNet (an electronic lexical database) in the process of ontology matching.

119 citations

Proceedings Article
01 Jan 2004
TL;DR: This paper proffers the use of information-extraction ontologies as an approach that may lead to semantic understanding.
Abstract: Information is ubiquitous, and we are flooded with more than we can process Somehow, we must rely less on visual processing, point-and-click navigation, and manual decision making and more on computer sifting and organization of information and automated negotiation and decision making A resolution of these problems requires software with semantic understanding---a grand challenge of our timeMore particularly, we must solve problems of automated interoperability, integration, and knowledge sharing, and we must build information agents and process agents that we can trust to give us the information we want and need and to negotiate on our behalf in harmony with our beliefs and goalsThis paper proffers the use of information-extraction ontologies as an approach that may lead to semantic understanding

119 citations

Proceedings ArticleDOI
09 Nov 2007
TL;DR: A semi-automatable method aimed to find the business multidimensional concepts from a domain ontology representing different and potentially heterogeneous data sources of the authors' business domain is proposed.
Abstract: This paper presents a new approach to automate the multidimensional design of Data Warehouses. In our approach we propose a semi-automatable method aimed to find the business multidimensional concepts from a domain ontology representing different and potentially heterogeneous data sources of our business domain.In short, our method identifies business multidimensional concepts from heterogeneous data sources having nothing in common but that they are all described by an ontology.

119 citations

Journal ArticleDOI
TL;DR: Two generic cases including novel integration algorithms, namely the integration of two heterogeneous vector data sets, and the Integration of raster and vector data are described, linked to a federated database which allows for automatic object matching and for managing n:m relationships.
Abstract: The integration of heterogeneous geospatial data offers possibilities to manually and automatically derive new information, which are not available when using only a single data source. Furthermore, it allows for a consistent representation and the propagation of updates from one data set to the other. However, different acquisition methods, data schemata and updating cycles of the content can lead to discrepancies in geometric and thematic accuracy and correctness which hamper the combined integration. To overcome these difficulties, appropriate methods for the integration and harmonization of data from different sources and of different types are needed. In this paper we describe two generic cases including novel integration algorithms, namely the integration of two heterogeneous vector data sets, and the integration of raster and vector data. Both algorithms are linked to a federated database which allows for automatic object matching and for managing n:m relationships. We describe and illustrate our work using vector data from topography and the geosciences, as well as multi-spectral imagery. © 2007 International Society for Photogrammetry andRemote Sensing, Inc. (ISPRS). Published byElsevier B.V. All rights reserved.

118 citations

Proceedings ArticleDOI
07 Apr 2015
TL;DR: An ontology developed for a cyber security knowledge graph database is described to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain.
Abstract: In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.

118 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
2022149
202111
202011
201919
201843