Title: A scalable automatic service discovery approach based on probabilistic topic model

Authors: Yuan Yuan; Weishi Zhang; Xiuguo Zhang

Addresses: School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China ' School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China ' School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

Abstract: Current service discovery approaches mainly focus on syntax matchmaking, which contains little semantic information to discover services automatically. This paper proposes a scalable automatic service discovery approach based on probabilistic topic model. Specifically, a novel service description model PTWSDM is proposed. With this model, heterogeneous service descriptions can be represented in a topic vector form on the same homogeneous plane. For the scarcity of word co-occurrence patterns in service functional descriptions, Biterm topic model is introduced to extract latent topics. Finally, a stream algorithm for topic model updating is introduced in order that the proposed approach is scalable and adaptable for large-scale dynamic registry. Experimental results confirm that the proposed approach outperforms the state-of-the-art solutions in terms of precision and normalised discounted cumulative gain values. It also has good time performance and scalability.

Keywords: service discovery; service description model; probabilistic topic model; Biterm topic model; scalability; word co-occurrence patterns; latent topics; stream algorithm.

DOI: 10.1504/IJWGS.2016.080133

International Journal of Web and Grid Services, 2016 Vol.12 No.4, pp.349 - 369

Received: 06 Jan 2016
Accepted: 17 Jan 2016
Published online: 04 Nov 2016 *

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