A Framework for Document-driven Evolution of Ontology

2008

One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery.

Proceedings of the European …, 2002

2005

The Semantic Web initiative aims at automating semantics' embedding in Web pages so that richer information retrieval, data integration and improved navigation can be supported. Domain ontologies are used in this direction, providing a way to semantically characterizing Web documents if a mapping of the documents to the ontology concepts can be managed. Given such an ontology, the main problem that arises due to the rapid changes of the Web resources is monitoring the domain changes and update the given ontology respectively. In this paper we propose a semi-automated method for ontology evolution using documents' clustering, from the results of which ontology enrichments and updates are extracted.

2005

A text corpus analysis method is proposed for analyzing a branch of knowledge evolving in time. We apply this method to the case of parallel computing, which has shown a great progress during the last 15 years. For this, we construct a rapid prototype of ontology (RPO) of this domain, based on a large collection of different types of documents. We demonstrate the possibility of embodying the RPO idea and its usefulness for revealing prospects for research in different areas of parallel computing.

Information semantics and semantic interoperability among applications, systems, system agents, and web services are mostly based on ontology. Its increase usage in Information Systems and Knowledge Sharing Systems raises the importance of ontology maintenance. Ontology change management is a collaborative process that incorporates areas like ontology engineering, evolution versioning, merging, integration, and maintenance. Changes are made to the body of knowledge as experts develop a better understanding of the domain. As a result, the body of knowledge evolves from one state to another. Preserving consistency, while accommodating new changes, is a crucial task that needs special attention. This paper aims at providing a comprehensive review on key approaches followed in the field of ontology evolution. The analysis reveals that different individual components have been developed but a complete integrated system for automated ontology evolution is not available yet. This paper also introduces some unfolded challenges in the field of ontology evolution, which must be tackled to complete the process automatically. Moreover, the new changes (evolved ontology) could affect the dependent data, applications, systems, and services. Therefore, this paper also discusses in detail why special attention must be paid to minimize the after effects of ontology evolution and proposes some possible solutions to achieve this goal.

Techniques et sciences informatiques, 2012

Ontologies have become an important means for structuring knowledge and defining semantic information retrieval systems. Ontology engineering requires a significant effort, and recent researches show that human language technologies are useful means to acquire or update ontologies from text. In this paper we present DYNAMO, a tool based on a Multi-Agent System, which aims at assisting ontologists during the ontology building and evolution processes. This work is carried out in the context of the DYNAMO project. The main novelty of the agent system is to take advantage of text extracted terms and lexical relations together with some quantitative features of the corpus to guide the agents when self-organizing. We exhibit the first experiment of ontology building that shows promising results, and helps us to identify key issues to be solved to the DYNAMO system behavior and the resulting ontology.

Abstract. Ontology based modeling is becoming increasingly important in the design of complex knowledge management applications. However, many problems related to large-scale ontology development, deployment and collaborative maintenance still remain to be solved, in particular when ontology-based applications create huge amounts of descriptive metadata which refer to the concepts of an evolving ontology.

2006

We propose a method for semi-automatic construction of an ontology of a given branch of science for measuring its evolution in time. The method relies on a collection of documents in the given thematic domain. We observe that the words of different levels of abstraction are located within different parts of a document: say, the title or abstract contains more general words than the body of the paper.

Communications in Computer and Information Science, 2009

Information semantics and semantic interoperability among applications, systems, and services are mostly based on ontology. Its increase usage in Information Systems and Knowledge Sharing Systems raises the importance of ontology maintenance. Ontology change management incorporates areas like ontology engineering, evolution versioning, merging, integration, and maintenance. Changes are made to the body of knowledge as experts develop a better understanding of the domain. As a result, the body of knowledge evolves from one state to another. Preserving consistency, while accommodating new changes, is a crucial task that needs special attention. This paper aims at providing a comprehensive review on key approaches followed in the field of ontology evolution. The analysis reveals that different individual components have been developed but a complete integrated system for automated ontology evolution is not available yet. This paper introduces some unfolded challenges in the field of ontology evolution, which must be tackled to complete the process automatically. Moreover, the new changes could affect the dependent data, applications, systems, and services. Therefore, this paper also discusses in detail why special attention must be paid to minimize the after effects of ontology evolution and proposes some possible solutions to achieve this goal.

2007

ABSTRACT The algorithms dealing with the incorporation of new knowledge in an ontology often share a rather standard process of dealing with changes. This process consists of the determination of the allowed change operations, the identification of the inconsistencies that could be caused by each such operation as well as the various alternatives to deal with each such inconsistency, and, finally, some (manual or automatic) selection mechanism that allows the determination of the “best” of these alternatives.