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A Spatial and Sequential Combined Method for Web Service Classification

conference contribution
posted on 2020-01-01, 00:00 authored by X Wang, J Liu, Xiao LiuXiao Liu, X Cui, H Wu
With the growing prosperity of the Web service ecosystem, high-quality service classification has become an essential requirement. Web service description documents are semantic definitions of services, which is edited by service developers to include not only usage scenarios and functions of services but also a lot of prior knowledge and jargons. However, at present, existing deep learning models cannot fully extract the heterogeneous features of service description documents, resulting in unsatisfactory service classification results. In this paper, we propose a novel deep neural network which integrates the Graph Convolutional Network (GCN) with Bidirectional Long Short-Term Memory (Bi-LSTM) network to automatically extract the features of function description documents for Web services. Specifically, we first utilize a two-layer GCN to extract global spatial structure features of Web services, which serves as a pre-training word embedding process. Afterwards, the sequential features of Web services learned from the Bi-LSTM model are integrated for joint training of parameters. Experimental results demonstrate that our proposed method outperforms various state-of-the-art methods in classification performance.

History

Volume

12317

Pagination

764-778

Location

Tianjin, China

Start date

2020-09-18

End date

2020-09-20

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030602581

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Wang X, Zhang R, Lee Y-K, Sun L, Moon Y-S

Title of proceedings

APWeb-WAIM 2020 : Proceedings of the 4th Asia-Pacific and Web-Age Information Management International Joint Conference on Web and Big Data

Event

Asia-Pacific and Web-Age Information Management. Joint International Conference on Web and Big Data (4th : 2020 ; Tianjin, China)

Publisher

Springer

Place of publication

Chan, Switzerland

Series

Lecture Notes in Computer Science

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