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Probabilistic Matchmaking Methods for Automated Service Discovery

Cassar, G, Barnaghi, P and Moessner, K (2013) Probabilistic Matchmaking Methods for Automated Service Discovery IEEE Transactions on Service Computing.

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Abstract

Automated service discovery enables human users or software agents to form queries and to search and discover the services based on different requirements. This enables implementation of high-level functionalities such as service recommendation, composition, and provisioning. The current service search and discovery on the Web is mainly supported by text and keyword based solutions which offer very limited semantic expressiveness to service developers and consumers. This paper presents a method using probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors are used to construct a model to represent different types of service descriptions in a vector form. With this transformation, heterogeneous service descriptions can be represented, discovered, and compared on the same homogeneous plane. The proposed solution is scalable to large service datasets and provides an efficient mechanism that enables publishing and adding new services to the registry and representing them using latent factors after deployment of the system. We have evaluated our solution against logic-based and keyword-based service search and discovery solutions. The results show that the proposed method performs better than other solutions in terms of precision and normalised discounted cumulative gain values.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Authors :
AuthorsEmailORCID
Cassar, GUNSPECIFIEDUNSPECIFIED
Barnaghi, PUNSPECIFIEDUNSPECIFIED
Moessner, KUNSPECIFIEDUNSPECIFIED
Date : 13 May 2013
Identification Number : 10.1109/TSC.2013.28
Additional Information : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 17 May 2013 16:17
Last Modified : 23 Sep 2013 20:07
URI: http://epubs.surrey.ac.uk/id/eprint/771423

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