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Theoretical Characteristics of Ontology Learning Algorithm in Multi-dividing Setting.
- Source :
- IAENG International Journal of Computer Science; Jun2016, Vol. 43 Issue 2, p184-191, 8p
- Publication Year :
- 2016
-
Abstract
- Ontology, as a useful tool, has been widely applied in various fields, and ontology concept similarity calculation is an essential problem in these application algorithms. A recent method to get similarity between vertices on ontology is not by pairwise computation but based on a function which maps ontology graph into a line and maps every vertex in graph into a real-value, the similarity is measured by the difference of their corresponding scores. The multi-dividing method is suitable for ontology problem and plays a key role in achieving this. Such ontology function is given by learning a training sample which contains a subset of vertices with k classes from ontology graph. In this paper, we propose a new multi-dividing ontology algorithm framework, which is designed to avoid the choice of loss function. Meanwhile, there is such a vertex selection policy in new multi-dividing ontology algorithm that it guarantees that the new algorithm can be employed for an ontology graph with its structure rather than a tree. We provide some theoretical characteristics of the new multi-dividing ontology algorithm, and show that the new algorithm is convergent. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1819656X
- Volume :
- 43
- Issue :
- 2
- Database :
- Supplemental Index
- Journal :
- IAENG International Journal of Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 115857331