Open Access
2013 Stability Analysis of Learning Algorithms for Ontology Similarity Computation
Wei Gao, Tianwei Xu
Abstr. Appl. Anal. 2013(SI32): 1-9 (2013). DOI: 10.1155/2013/174802


Ontology, as a useful tool, is widely applied in lots of areas such as social science, computer science, and medical science. Ontology concept similarity calculation is the key part of the algorithms in these applications. A recent approach is to make use of similarity between vertices on ontology graphs. It is, instead of pairwise computations, based on a function that maps the vertex set of an ontology graph to real numbers. In order to obtain this, the ranking learning problem plays an important and essential role, especially k-partite ranking algorithm, which is suitable for solving some ontology problems. A ranking function is usually used to map the vertices of an ontology graph to numbers and assign ranks of the vertices through their scores. Through studying a training sample, such a function can be learned. It contains a subset of vertices of the ontology graph. A good ranking function means small ranking mistakes and good stability. For ranking algorithms, which are in a well-stable state, we study generalization bounds via some concepts of algorithmic stability. We also find that kernel-based ranking algorithms stated as regularization schemes in reproducing kernel Hilbert spaces satisfy stability conditions and have great generalization abilities.


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Wei Gao. Tianwei Xu. "Stability Analysis of Learning Algorithms for Ontology Similarity Computation." Abstr. Appl. Anal. 2013 (SI32) 1 - 9, 2013.


Published: 2013
First available in Project Euclid: 26 February 2014

zbMATH: 1371.68234
MathSciNet: MR3064520
Digital Object Identifier: 10.1155/2013/174802

Rights: Copyright © 2013 Hindawi

Vol.2013 • No. SI32 • 2013
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