MaLM: Machine learning middleware to tackle ontology heterogeneity.
(pp. pp. 449-454).
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.
|Title:||MaLM: Machine learning middleware to tackle ontology heterogeneity|
|Open access status:||An open access version is available from UCL Discovery|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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