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MaLM: Machine learning middleware to tackle ontology heterogeneity

Capra, L; (2007) MaLM: Machine learning middleware to tackle ontology heterogeneity. In: (pp. pp. 449-454). Green open access


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We envisage pervasive computing applications to be predominantly engaged in knowledge-based interactions, where services and information will be found and exchanged based on some formal knowledge representation. To enable knowledge sharing and reuse, current middle-ware make the assumption that a single, universally accepted, ontology exists with which queries and assertions are exchanged. We argue that such an assumption is unrealistic. Rather, different communities will speak different 'dialects'; in order to enable cross-community interactions, thus increasing the range of services and information available to users, on-the-fly translations are required. In this paper we introduce MaLM, a middleware for pervasive computing devices that exploits an unsupervised machine learning technique called Self-Organising Map to tackle the problem of ontology heterogeneity. At any given time, the MaLM instance running on a device operates in one of two possible modes: 'training', that is, MaLM is autonomically learning how to group together semantically closed concepts; and 'expert', that is, given in input a query or assertion expressed in a foreign dialect, MaLM identifies the concept, expressed in the device mother-tongue, that most closely represents it. © 2007 IEEE.

Type: Proceedings paper
Title: MaLM: Machine learning middleware to tackle ontology heterogeneity
ISBN: 0769527884
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/PERCOMW.2007.64
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/7345
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