Abstract and Applied Analysis

Stability Analysis of Learning Algorithms for Ontology Similarity Computation

Wei Gao and Tianwei Xu

Full-text: Open access

Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 174802, 9 pages.

Dates
First available in Project Euclid: 26 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393450313

Digital Object Identifier
doi:10.1155/2013/174802

Mathematical Reviews number (MathSciNet)
MR3064520

Zentralblatt MATH identifier
1371.68234

Citation

Gao, Wei; Xu, Tianwei. Stability Analysis of Learning Algorithms for Ontology Similarity Computation. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 174802, 9 pages. doi:10.1155/2013/174802. https://projecteuclid.org/euclid.aaa/1393450313


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