Ontology based data integration

Ontology based data integration
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Ontology based Data Integration involves the use ofontology(s) to effectively combine data and/or information from multiple heterogeneous sources[1]. It is one of the multipledata integration approaches and may be classified as Local-As-View (LAV)[2]. The effectiveness of ontology based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process.
Contents
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1 Background2 The Role of Ontologies2.1 Approaches using ontologies for Data Integration
3 See also4 References
[edit] Background
Data from multiple sources are characterized by multiple types of heterogeneity. The following hierarchy is often used[3][4]:
Syntactic Heterogeneity: is a result of differences in representation format of dataSchematic or Structural Heterogeneity: the native model or structure to store data differ indata sources leading to structural heterogeneity. Schematic heterogeneity that particularly appears in structured databases is also an aspect of structural heterogeneity[3].Semantic Heterogeneity: differences in interpretation of the 'meaning' of data are source of semantic heterogeneitySystem Heterogeneity: use of differentoperating system, hardware platforms lead to system heterogeneity
Ontologies, as formal models of representation with explicitly defined concepts and named relationships linking them, are used to address the issue ofsemantic heterogeneity in data sources. In domains likebioinformatics andbiomedicine, the rapid development, adoption and public availability of ontologies[1]has made it possible for thedata integration community to leverage them forsemantic integration of data and information.
[edit] The Role of Ontologies
Ontologies enable the unambiguous identification of entities in heterogeneous information systems and assertion of applicable named relationships that connect these entities together. Specifically, ontologies play the following roles:
Content Explication[1]
The ontology enables accurate interpretation of data from multiple sources through the explicit definition of terms and relationships in the ontology.
Query Model[1]
In some systems like SIMS[5], the query is formulated using the ontology as a global query schema.
Verification[1]
The ontology verifies the mappings used to integrate data from multiple sources. These mappings may either be user specified or generated by a system.
[edit] Approaches using ontologies for Data Integration
There are three main architectures that are implemented in ontology based data integration applications[1] namely,
Single Ontology approachA single ontology is used as a global reference model in the system. This is the simplest approach as it can be simulated by other approaches[1]. SIMS[5] is a prominent example of this approach.
Multiple OntologiesMultiple ontologies, each modeling an individual data source, are used in combination for integration. Though, this approach is more flexible than the single ontology approach, it requires creation of mappings between the multiple ontologies. Ontology mapping is a challenging issue and is focus of large number of research efforts incomputer science[2]. The OBSERVER system[6] is an example of this apporach.
Hybrid approachesThe hybrid approach involves the use of multiple ontologies that subscribe to a common, top-level vocabulary[7]. The top level vocabulary defines the basic terms of the domain. Thus, the hybrid approach makes it easier to use multiple ontologies for integration in presence of the common vocabulary.
[edit] See also
Schema matchingData IntegrationEnterprise application integrationEnterprise Information IntegrationData mappingOntology mappingSemantic Integration
[edit] References
^abcdef H. Wache, T. Vögele, U. Visser, H. Stuckenschmidt, G. Schuster, H. Neumann, S. Hübner (2001). "Ontology-Based Integration of Information A Survey of Existing Approaches".. ^ Maurizio Lenzerini (2002). "Data Integration: A Theoretical Perspective".: 243-246.  ^ab A.P. Sheth (1999). Changing Focus on Interoperability in Information Systems: From System, Syntax, Structure to Semantics, 5-30. ^AHM02 Tutorial 5: Data Integration and Mediation; Contributors: B. Ludaescher, I. Altintas, A. Gupta, M. Martone, R. Marciano, X. Qian ^ab Y. Arens, C. Hsu, C.A. Knoblock (1996). "Query Processing in sims information mediator".. ^ E. Mena, V. Kashyap, A. Sheth, A. Illarramendi (1996). "OBSERVER: An Approach for Query Processing in Global Information Systems based on Interoperation across Pre-existing Ontologies".. ^ Cheng Hian Goh (1997). "Representing and Reasoning about Semantic Conflicts in Heterogeneous Information Systems"..
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Categories:Ontology (computer science) |Data management