Specificity-Aware Ontology Generation for Improving Web Service Clustering
Summary :
With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.8 pp.2035-2043
- Publication Date
- 2018/08/01
- Publicized
- 2018/05/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7395
- Type of Manuscript
- PAPER
- Category
- Data Engineering, Web Information Systems
Authors
Rupasingha A. H. M. RUPASINGHA
University of Aizu
Incheon PAIK
University of Aizu
Banage T. G. S. KUMARA
Sabaragamuwa University of Sri Lanka
Keyword
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Rupasingha A. H. M. RUPASINGHA, Incheon PAIK, Banage T. G. S. KUMARA, "Specificity-Aware Ontology Generation for Improving Web Service Clustering" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 8, pp. 2035-2043, August 2018, doi: 10.1587/transinf.2017EDP7395.
Abstract: With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7395/_p
Copy
@ARTICLE{e101-d_8_2035,
author={Rupasingha A. H. M. RUPASINGHA, Incheon PAIK, Banage T. G. S. KUMARA, },
journal={IEICE TRANSACTIONS on Information},
title={Specificity-Aware Ontology Generation for Improving Web Service Clustering},
year={2018},
volume={E101-D},
number={8},
pages={2035-2043},
abstract={With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.},
keywords={},
doi={10.1587/transinf.2017EDP7395},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Specificity-Aware Ontology Generation for Improving Web Service Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 2035
EP - 2043
AU - Rupasingha A. H. M. RUPASINGHA
AU - Incheon PAIK
AU - Banage T. G. S. KUMARA
PY - 2018
DO - 10.1587/transinf.2017EDP7395
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2018
AB - With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.
ER -
English
