Tatt‐BiLSTM: Web service classification with topical attention‐based BiLSTM

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

Download Full-text

Augmenting Labeled Probabilistic Topic Model for Web Service Classification

International Journal of Web Services Research ◽

10.4018/ijwsr.2019010105 ◽

2019 ◽

Vol 16 (1) ◽

pp. 93-113 ◽

Cited By ~ 3

Author(s):

Shengye Pang ◽

Guobing Zou ◽

Yanglan Gan ◽

Sen Niu ◽

Bofeng Zhang

Keyword(s):

Web Service ◽

Classification Accuracy ◽

Topic Model ◽

Service Providers ◽

Classification Model ◽

Model Learning ◽

Data Set ◽

Management Platform ◽

Probabilistic Topic Model ◽

Web Service Classification

Web service classification has become an urgent demand on service-oriented applications. Most existing classification algorithms mainly rely on the original service descriptions. That leads to low classification accuracy, since it cannot fully reflect the semantic feature specific to a service category. To solve the issue, this article proposes a novel approach for web service classification, including service topic feature extraction, service functionality augmentation, and service classification model learning. The characteristic is that the original service descriptions can be semantically augmented, which is fed to deriving a service classifier via labeled probabilistic topic model. A benefit from this approach is that it can be applied to an online service management platform, where it assists service providers to facilitate the registration process. Extensive experiments have been conducted on a large-scale real-world data set crawled from ProgrammableWeb. The results demonstrate that it outperforms state-of-the-art methods in terms of service classification accuracy and convergence speed.

Download Full-text