Customised Concept Weighting: A Neural Network Approach.

The aim of concept weighting in ontologies or in other data graphs is to characterise the importance of each concept in a specific domain, and to determine its selective power. This is particularly useful for data analysis tasks. Existing works on concept weighting mainly exploit either the graph’s structure or the frequency of the concept in the data instances. These works provide concept weights independently form the considered analysis task. We argue that these weights should vary according to the targeted task, and we introduce a neural network based approach which computes concept weights using regression on a customised multi-layered structure. The loss function used in the approach is specified according to a given labelling of the elements in the considered dataset. In this paper, we present the principles of our weighting approach and we report on some experiments showing its effectiveness on real data extracted from the national library of France describing the documents’ conservation histories.