Ontology based Data Integration involves the use of ontology(s) to effectively combine data and/or information from multiple heterogeneous sources ^[1]. It is one of the multiple Data Integration approaches and may be classified as Local As View (LAV)^[2]. The effectiveness of ontology based data integration is closely tied to the quality of ontology used in the integration process. // In philosophy, ontology (from the Greek , genitive : of being (part. ... Data integration is the process of combining data residing at different sources and providing the user with a unified view of these data [1]. This process emerges in a variety of situations both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different...

Background

Data from multiple sources are characterized by multiple types of heterogeneity. The Distributed Databases community has categorized them into the following types of heterogeneity ^[3] namely,

• Syntactic Heterogeneity: is a result of differences in representation format of data
• Schematic or Structural Heterogeneity: the native model or structure to store data differ in data 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 heterogeneity
• System Heterogeneity: use of different operating system, hardware platforms lead to system heterogeneity

Ontology, as formal model of representation with explicilty defined concepts and named relationships linking them, is used to address the issue of semantic heterogeneity in data sources. In domains like bioinformatics and biomedicine, the rapid development, adoption and public availability of ontologies [1]has made it possible for the data integration community to leverage them for semantic integration of data and information. An operating system (OS) is a set of computer programs that manage the hardware and software resources of a computer. ... In both computer science and information science, an ontology is a data model that represents a domain and is used to reason about the objects in that domain and the relations between them. ... Map of the human X chromosome (from the NCBI website). ... See drugs, medication, and pharmacology for substances that treat patients. ... In both computer science and information science, an ontology is a data model that represents a domain and is used to reason about the objects in that domain and the relations between them. ... Data integration is the process of combining data residing at different sources and providing the user with a unified view of these data [1]. This process emerges in a variety of situations both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different... In Enterprise Application Integration, semantic integration is the process of using business semantics to automate the communication between computer systems. ...

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 ^[4], 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.

Approaches using ontologies for Data Integration

There are three main architectures that are implemented in ontology based data integration applications ^[1] namely,

Single Ontology approach

A 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 ^[4] is a prominent example of this approach.

Multiple Ontologies

Multiple 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 in computer science [2]. The OBSERVER system ^[5] is an example of this apporach.

Hybrid approaches

The hybrid approach involves the use of multiple ontologies that subscribe to a common, top-level vocabulary ^[6]. 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 vacabulary.

Computer scaence, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. ...

See also

• Data Integration
• Enterprise application integration
• Enterprise Information Integration
• Data mapping
• Semantic Integration

Data integration is the process of combining data residing at different sources and providing the user with a unified view of these data [1]. This process emerges in a variety of situations both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different... Enterprise Application Integration (EAI) is defined as the uses of software and computer systems architectural principles to integrate a set of enterprise computer applications. ... EII is the industry acronym for Enterprise Information Integration. ... Data mapping is the process of creating data element mappings between two distinct data models. ... In Enterprise Application Integration, semantic integration is the process of using business semantics to automate the communication between computer systems. ...

References

1. ^ ^{a b c d e f} 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". {{{booktitle}}}. 
2. ^ Maurizio Lenzerini (2002). "Data Integration: A Theoretical Perspective". {{{booktitle}}}: 243-246. 
3. ^ ^{a b} A.P. Sheth (1999). Changing Focus on Interoperability in Information Systems: From System, Syntax, Structure to Semantics, 5-30. 
4. ^ ^{a b} Y. Arens, C. Hsu, C.A. Knoblock (1996). "Query Processing in sims information mediator". {{{booktitle}}}. 
5. ^ 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". {{{booktitle}}}. 
6. ^ Cheng Hian Goh (1997). "Representing and Reasoning about Semantic Conflicts in Heterogeneous Information Systems". {{{booktitle}}}.