<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this article, we propose an ontology learning algorithm for ontology similarity measure and ontology mapping in view of distance function learning techniques. Using the distance computation formulation, all the pairs of ontology vertices are mapped into real numbers which express the distance of their corresponding vectors. The more distance between two vertices, the smaller similarity between their corresponding concepts. The stabilities of our learning algorithm are defined and several bounds are yielded via stability assumptions. The simulation experimental conclusions show that the new proposed ontology algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in certain engineering applications.</p></abstract>

In this article, we propose an ontology learning algorithm for ontology similarity measure and ontology mapping in view of distance function learning techniques. Using the distance computation formulation, all the pairs of ontology vertices are mapped into real numbers which express the distance of their corresponding vectors. The more distance between two vertices, the smaller similarity between their corresponding concepts. The stabilities of our learning algorithm are defined and several bounds are yielded via stability assumptions. The simulation experimental conclusions show that the new proposed ontology algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in certain engineering applications.

Ontology optimization tactics via distance calculating

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Ontology optimization tactics via distance calculating"}

In this article, we propose an ontology learning algorithm for ontology similarity measure and ontology mapping in view of distance function learning...

	P@1 average precision ratio	P@3 average precision ratio	P@5 average precision ratio
Our Algorithm	0.6913	0.7556	0.9161
Algorithm in [13]	0.6129	0.7312	0.7935
Algorithm in [14]	0.6913	0.7556	0.8452
Algorithm in [26]	0.6774	0.7742	0.8968

	P@1 average precision ratio	P@3 average precision ratio	P@5 average precision ratio
Our Algorithm	0.2778	0.5000	0.7667
Algorithm in [24]	0.2778	0.4815	0.5444
Algorithm in [13]	0.2222	0.4074	0.4889
Algorithm in [14]	0.2778	0.4630	0.5333

	P@3 average precision ratio	P@5 average precision ratio	P@10 average precision ratio
Our Algorithm	0.5358	0.6517	0.8821
Algorithm in [12]	0.4549	0.5117	0.5859
Algorithm in [13]	0.4282	0.4849	0.5632
Algorithm in [14]	0.4831	0.5635	0.6871

	P@3 average precision ratio	P@5 average precision ratio	P@10 average precision ratio	P@20 average precision ratio
Our Algorithm	0.4987	0.6364	0.7602	0.8546
Algorithm in [13]	0.4638	0.5348	0.6234	0.7459
Algorithm in [14]	0.4356	0.4938	0.5647	0.7194
Algorithm in [25]	0.4213	0.5183	0.6019	0.7239

	P@1 average precision ratio	P@3 average precision ratio	P@5 average precision ratio
Our Algorithm	0.5714	0.6786	0.7714
Algorithm in [12]	0.5000	0.5952	0.6857
Algorithm in [13]	0.4286	0.5238	0.6071
Algorithm in [14]	0.5714	0.6429	0.6500

Ontology optimization tactics via distance calculating

Online veröffentlicht: 29. Jan. 2016

Seitenbereich: 159 - 174

Eingereicht: 03. Okt. 2015

Akzeptiert: 28. Jan. 2016

DOI: https://doi.org/10.21042/AMNS.2016.1.00012

© 2016 Yun Gao, Mohammad Reza Farahani, Wei Gao

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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Tab. 4. The Experiment Results of Ontology Mapping

Tab. 2. The Experiment Results of Ontology Mapping

Tab. 1.The Experiment Results of Ontology Similarity measure

Tab. 3. The Experiment Results of Ontology Similarity measure

Tab. 5. The Experiment Results of Ontology Mapping