Ontology-based data integration involves the use of one or more
ontologies to effectively combine data or information from multiple heterogeneous sources.
It is one of the multiple
data integration approaches and may be classified as Global-As-View (GAV).
The effectiveness of ontology‑based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process.
Background
Data from multiple sources are characterized by multiple types of heterogeneity. The following hierarchy is often used:
*
Syntactic heterogeneity: is a result of differences in representation format of data
* Schematic or
structural heterogeneity
A structure is an arrangement and organization of interrelated elements in a material object or system, or the object or system so organized. Material structures include man-made objects such as buildings and machines and natural objects such as ...
: 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.
*
Semantic heterogeneity: differences in interpretation of the 'meaning' of data are source of semantic heterogeneity
*
System heterogeneity
A system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. A system, surrounded and influenced by its environment, is described by its boundaries, structure and purpose and expressed ...
: use of different
operating 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 of
semantic heterogeneity in data sources. In domains like
bioinformatics
Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combi ...
and
biomedicine, the rapid development, adoption and public availability of ontologie
has made it possible for the
data integration community to leverage them for
semantic integration of data and information.
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:
The ontology enables accurate interpretation of data from multiple sources through the explicit definition of terms and relationships in the ontology.
;Query Model:
In some systems like SIMS,
the query is formulated using the ontology as a global query schema.
;Verification:
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,
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.
SIMS
a prominent example of this approach. The Structured Knowledge Source Integration component of
Research Cyc is another prominent example of this approach. (Title = Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries). The Gellish Taxonomic Dictionary-Ontology follows this approach as well.
;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 sciencebr>
The OBSERVER system
is an example of this approach.
;Hybrid approaches: The hybrid approach involves the use of multiple ontologies that subscribe to a common, top-level vocabulary.
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.
See also
*
Data mapping
*
Enterprise application integration
*
Enterprise information integration
*
Ontology mapping
*
Schema matching
Further reading
*{{cite journal , last1 = Chicco , first1 = D , last2 = Masseroli , first2 = M , year = 2016 , title = Ontology-based prediction and prioritization of gene functional annotations , journal = IEEE/ACM Transactions on Computational Biology and Bioinformatics , volume = 13 , issue = 2 , pages = 248–260 , doi=10.1109/TCBB.2015.2459694 , pmid = 27045825 , s2cid = 2795344 , url = https://doi.org/10.1109/TCBB.2015.2459694
References
External links
OBSERVER home pageCyc Semantic Knowledge Source Integration (SKSI)
Ontology (information science)
Data management
Data integration