Boon Toh Low

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Combining models learned from multiple batches of data provide an alternative to the common practice of learning one model from all the available data (i.e. the data combination approach). This paper empirically examines the base-line behavior of the model combination approach in this multiple-data-batches scenario. We find that model combination can lead(More)
Distributed knowledge based applications in open domain rely on common sense infor­ mation which is bound to be uncertain and incomplete. To draw the useful conclusions from ambiguous data, one must address un­ certainties and conflicts incurred in a holis­ tic view. No integrated frameworks are vi­ able without an in-depth analysis of con­ flicts incurred(More)
The work on prototypes in ontologies pioneered by Rosch [10] and elaborated by Lakoff [8] and Freund [3] is related to vagueness in the sense that the more remote an instance is from a prototype the fewer people agree that it is an example of that prototype. An intuitive example is the prototypical “mother”, and it is observed that more specific instances(More)