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Probabilistic Methods for Service Clustering

Barnaghi, P, Cassar, G and Moessner, K Probabilistic Methods for Service Clustering In: SMR2 2010, 2010-11-08 - 2010-11-08, Shanghai, China.

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Abstract

This paper focuses on service clustering and uses service descriptions to construct probabilistic models for service clustering.We discuss how service descriptions can be enriched with machine-interpretable semantics and then we investigate how these service descriptions can be grouped in clusters in order to make discovery, ranking, and recommendation faster and more effective. We propose using Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) (i.e. two machine learning techniques used in Information Retrieval) to learn latent factors from the corpus of service descriptions and group services according to their latent factors. By creating an intermediate layer of latent factors between the services and their descriptions, the dimensionality of the model is reduced and services can be searched and linked together based on probabilistic methods in latent space. The model can cluster any newly added service with a direct calculation without requiring to re-calculate the latent variables or re-train the model.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Copyright © 2010 for the individual papers by the papers' authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Depositing User: Symplectic Elements
Date Deposited: 09 May 2012 12:44
Last Modified: 09 Jun 2014 13:18
URI: http://epubs.surrey.ac.uk/id/eprint/470681

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