Authors:
Theodora Galani
1
;
George Papastefanatos
2
and
Yannis Stavrakas
2
Affiliations:
1
RC ATHENA and NTUA, Greece
;
2
RC ATHENA, Greece
Keyword(s):
Change Management, Data Evolution, RDF(S).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Society, e-Business and e-Government
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
The dynamic nature of web data brings forward the need for maintaining data versions as well as identifying changes between them. In this paper, we deal with problems regarding understanding evolution, focusing on RDF(S) knowledge bases, as RDF is a de-facto standard for representing data on the web. We argue that revisiting past snapshots or the differences between them is not enough for understanding how and why data evolved. Instead, changes should be treated as first-class-citizens. In our view, this involves supporting semantically rich, user-defined changes that we call complex changes, as well as identifying the interrelations between them. In this paper, we present our perspective regarding complex changes, propose a declarative language for defining complex changes for RDF(S) knowledge bases, and show how this language is used to detect complex change instances among dataset versions.