Automatic web services classification based on rough set theory

Chen, Li; Zhang, Ying; Song, Zilin; Zhang, Miao

doi:10.1007/s11771-013-1787-1

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…This collection has been used as a benchmark for works that have proposed web services classification. Therefore, it is possible to compare our results with previously proposed approaches, such as Zhang and Pan [4], Wang et al [5], Chen et al [6] and Yuan and Jian [8]. In Table II The results show the effectiveness of our approach compared with other approaches under the same Web services collection and the same evaluation criteria.…”

Section: Experimentation and Resultsmentioning

confidence: 88%

“…These elements have been exploited for several purposes. As in [2] and [3] that have used text processing with parameters and operations names in order to obtain the similarity between web services; content-based approaches for web services classification have been proposed in [4], [5], [6], [7], [8] and [9], which are described below. Web services clustering from WSDL documents in order to facilitate web services discovery proposed in [10] and [11], also in [12] a clustering-based approach to web service categorization in order to form a hierarchy of service taxonomy is presented.…”

Section: Related Workmentioning

confidence: 99%

“…In [5], the classification of web services is based on support vector machine algorithm and it is achieved by calculating a similarity between words using WordNet and a domain taxonomy in order to reach an efficient classification of web services in the collection. A similar web services classification has been proposed in [6], in this case it is based on sets, they propose a representation of web service descriptions with vector space model and an entropy-based weighting of all terms.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Web Services Ontology Population through Text Classification

Reyes‐Ortiz

Bravo

Pablo

2016

Annals of Computer Science and Information Systems

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Abstract-In this paper, we describe the process by which web services ontologies are populated from a web services collection. The general approach relies on a global ontology model that is used to represent automatically web services. The model is enriched with web service instances classified into a taxonomy. The main idea is to extract taxonomic relations (isTypeOf ) from web services using a supervised classifier of textual descriptions attached to web services. The entire process for ontology population involves the following tasks: text extraction from web service descriptions, classification of text descriptions and extraction of taxonomic relations (instances of classified web services). An experimentation was carried out with a collection of web service, which shows promising results and the feasibility of our approach.

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Section: Experimentation and Resultsmentioning

confidence: 88%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Web Services Ontology Population through Text Classification

Reyes‐Ortiz

Bravo

Pablo

2016

Annals of Computer Science and Information Systems

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“…Diversos trabajos han utilizado esta información para una gran variedad de tareas relacionadas al procesamiento de textos. Una de estas tareas es calcular la similitud semántica entre servicios [2,3,8,10,15], clasificar los servicios basada en su contenido [4,5,11,12,14,17,19] y agrupamiento de servicios Web [16].…”

Section: Trabajos Relacionadosunclassified

Clasificación de servicios Web mediante una red neuronal artificial usando n-gramas de palabras

Reyes‐Ortiz¹,

Bravo

2015

RCS

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Resumen. Este artículo presenta un enfoque basado en n-gramas de palabras para la clasificación automática de servicios Web utilizando una red neuronal artificial de tipo perceptrón multicapa. Los servicios Web contienen información de gran utilidad para lograr una clasificación basada en la funcionalidad del mismo. El enfoque se basa en n-gramas de palabras extraídas de la descripción del servicio web para determinar su pertenencia a una categoría. La experimentación realizada muestra resultados prometedores, logrando una clasificación con una medida F= 0.91 utilizando unigramas (1gramas) de palabras (características compuestas por una unidad léxica) y una ponderación TF-IDF. Palabras clave: Servicios web, modelo espacio vectorial, clasificación de servicio web, n-gramas de palabras, red neuronal perceptrón multicapa.

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“…Recent methods combine low-level features with others called high-level, which provide a closer representation of human perception, allowing to reach a semantic description of the images and achieve better results in their recovery. The main advances in this direction are linked to the rapid development of machine learning techniques and specifically to deep learning or deep learning [8].…”

Section: Introductionmentioning

confidence: 99%

Method for the Recovery of Indexed Images in Databases from Visual Content

Viloria

Varela

Vargas

et al. 2020

Computational Methods and Data Engineering

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The techniques of content-based image recovery (CBIR) provide a solution to a problem of information retrieval that may arise as follows: from an image of interest to recover or obtain similar images from among those present in a large collection, using only features or features extracted from said images Banuchitra and Kungumaraj (Int J Eng Comput Sci (IJECS) 5 (2016) [1]). Similar images are understood as those in which the same object or scene is observed with variations in perspective, lighting conditions or scale. The stored images are preprocessed and then their corresponding descriptors are indexed. The query image is also preprocessed to extract its descriptor, which is then compared to those stored by applying appropriate similarity measures, which allow the recovery of those images that are similar to the query image. In the present work, a method was developed for the recovery of indexed images in databases from their visual content, without the need to make textual annotations. Feature vectors were obtained from visual contents using artificial neural network techniques with deep learning.

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Automatic web services classification based on rough set theory

Cited by 11 publications

References 11 publications

Web Services Ontology Population through Text Classification

Web Services Ontology Population through Text Classification

Clasificación de servicios Web mediante una red neuronal artificial usando n-gramas de palabras

Method for the Recovery of Indexed Images in Databases from Visual Content

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