The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Com a expansão da Internet, o número de serviços Web disponíveis aumentou. O clustering de serviços Web para identificar clusters funcionalmente semelhantes tornou-se uma abordagem importante para a descoberta eficiente de serviços Web adequados. Neste estudo, propomos uma abordagem de agrupamento de serviços Web que utiliza aprendizagem de novas ontologias e um método de cálculo de similaridade baseado na especificidade de uma ontologia em um domínio em relação à teoria da informação. Em vez de utilizar métodos tradicionais, geramos a ontologia utilizando um novo método que considera a especificidade e similaridade dos termos. A especificidade de um termo descreve a quantidade de informações específicas do domínio contidas nesse termo. Embora os termos gerais contenham poucas informações específicas do domínio, os termos específicos podem conter muito mais informações relacionadas ao domínio. A ontologia gerada é utilizada nos cálculos de similaridade. Novos filtros baseados em lógica são introduzidos para o procedimento de cálculo de similaridade. Se os cálculos de similaridade usando os filtros especificados falharem, métodos baseados em recuperação de informações serão aplicados aos cálculos de similaridade. Finalmente, um algoritmo de agrupamento aglomerativo, baseado nos valores de similaridade calculados, é utilizado para o agrupamento. Alcançamos resultados altamente eficientes e precisos com esta abordagem de agrupamento, medidos por valores aprimorados de precisão média, recall, medida F, pureza e entropia. De acordo com os resultados, a especificidade dos termos desempenha um papel importante na classificação das informações do domínio. Nossa nova abordagem de agrupamento baseada em ontologias supera abordagens comparáveis existentes que não consideram a especificidade dos termos.
Rupasingha A. H. M. RUPASINGHA
University of Aizu
Incheon PAIK
University of Aizu
Banage T. G. S. KUMARA
Sabaragamuwa University of Sri Lanka
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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
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@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},}
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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 -