Authors:
Jon Atle Gulla
1
;
Geir Solskinnsbakk
1
;
Per Myrseth
2
;
Veronika Haderlein
2
and
Olga Cerrato
2
Affiliations:
1
Norwegian University of Science and Technology, Norway
;
2
Det Norske Veritas, Norway
Keyword(s):
Semantic Web, Ontology, Evolution, Text mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Metadata and Metamodeling
;
Ontology and the Semantic Web
;
Soft Computing
;
Symbolic Systems
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Mining
Abstract:
Ontology evolution is the process of incrementally and consistently adapting an existing ontology to changes in the relevant domain. Even though ontology management and versioning tools are now available, they are of limited use for ontology evolution unless the desired changes are known beforehand. Ontology learning toolsets are often employed, but they require large document sets and do not take the existing structures into account. Semantic drift refers to how concepts’ intentions gradually change as the domain evolves. When a semantic drift is detected, it means that a concept is gradually understood in a different way or its relationships with other concepts are undergoing some changes. A semantic drift captures small domain changes that are hard to detect with traditional ontology engineering approaches. This paper discusses a new approach to detecting and assessing semantic drift in ontologies. The method makes use of concept signatures that are constructed on the basis of
how concepts are used and described. Comparing how signatures change over time, we see how concepts’ semantic content evolves and how their relationships to other concepts gradually reflect these changes. An experiment with the DNV’s business sector ontology from 2004 and 2008 demonstrates the value of this approach to ontology evolution.
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