Authors:
Sergio de Cesare
;
George Foy
and
Mark Lycett
Affiliation:
Brunel University London, United Kingdom
Keyword(s):
Foundational Ontology, Perdurantist, 4D, Semantic Data Integration, Modelling, Graph Databases, Integration Frameworks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Coupling and Integrating Heterogeneous Data Sources
;
Data Engineering
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Model Driven Architectures and Engineering
;
Non-Relational Databases
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
;
Tools, Techniques and Methodologies for System Development
Abstract:
Although successfully employed as the foundation for a number of large-scale government and energy industry projects, foundational ontologies have not been widely adopted within mainstream Enterprise Systems (ES) data integration practice. However, as the closed-worlds of ES are opened to Internet scale data sources, there is an emerging need to better understand the semantics of such data and how they can be integrated. Foundational ontologies can help establish this understanding and therefore, there is a need to investigate how such ontologies can be applied to underpin practical ES integration solutions. This paper describes research undertaken to assess the effectiveness of such an approach through the development and application of the 4D-Semantic Extract Transform Load (4D-SETL) framework. 4D-SETL was employed to integrate a number of large scale datasets and to persist the resultant ontology within a prototype warehouse based on a graph database. The advantages of the approac
h included the ability to combine foundational, domain and instance level ontological objects within a single coherent system. Furthermore, the approach provided a clear means of establishing and maintaining the identity of domain objects as their constituent spatiotemporal parts unfolded over time, enabling process and static data to be combined within a single model.
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