Mashup tag completion with attention-based topic model
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ORIGINAL RESEARCH PAPER
Mashup tag completion with attention-based topic model Min Shi1
· Yufei Tang2 · Yu Huang2 · Maohua Lin3
Received: 10 December 2019 / Revised: 24 April 2020 / Accepted: 18 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract The past few years have witnessed a substantial increase in functional rich API services and their compositions (e.g., Mashup services) on the Internet, which as a result proposes new requirement of organization and management methods for better service understanding, discovery and usage. Tagging is known to be efficient for this purpose, and many tag recommender systems for API services have been proposed in the past. Existing approaches targeted at API services usually recommend several similar tags, which is insufficient for Mashup services that normally demonstrate diverse functionalities. In this paper, we propose a novel approach for Mashup service tag completion that can recommend tags revealing the functional features of Mashup services more comprehensively. It first extracts candidate tags for the target Mashup from API services in different functional domains. Then, it adopts a hybrid filtering procedure to recommend the most relevant tags. To support efficient tag extraction and recommendation, an attention-based topic model called Att-LDA is proposed that can highlight the functional oriented features in descriptions of services for accurate functional semantic learning. Experiments and validations on a real-word dataset demonstrate the performance of our approach improved 7.1% compared with other state-of-the-art methods. Keywords Web API service · Mashup service · Tag recommendation · Topic model
1 Introduction Nowadays, people are becoming increasingly aware of the great benefits brought by directly invoking existing application programming interfaces (API) services on the Web to solve business problems and develop diverse value-added software applications (e.g., Mashup services). On the other side, more and more companies like Google and Microsoft tend to share their software and data resources in the form of API services within and outside the organizations to promote work efficiency and meanwhile hook more Internet users. This keep-going trend has proposed new requirements for high-efficient service management methods to boost service function understanding and discovery. Among all methods,
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Min Shi [email protected]
1
School of Computer Science and Engineering, Hunan University of Science and Technology, Hunan, China
2
Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
3
Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, USA
tagging is widely accepted to be helpful [20,23], which is the behavior of annotating API (or Mashup) services with some meaningful terms to capture the functional properties reflected by service descriptions. To date, many methods have been proposed to automatically recommend tags for
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