A semantic case-based reasoning framework for text categorization
Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, volume 4825 of LNCS, page 729--742. Berlin, Heidelberg, Springer Verlag, (November 2007)Abstract
This paper presents a semantic case-based reasoning framework for text categorization. Text categorization is the task of classifying text documents under predened categories. Accidentology is our application eld and the goal of our framework is to classify documents describing real road accidents under predened road accident prototpypes, which also are described by text documents. Accidents are described by accident reports while accident prototypes are described by accident scenarios. Thus, text categorization is done by assigning each accident report to an accident scenario, which highlights particular mechanisms leading to accident.
We propose a textual case based reasoning approach (TCBR), which allows us to integrate both textual and domain knowledge aspects inorder to carry out this categorization. CBR solves a new problem (target case) by identifying its similarity to one or several previously solved problems (source cases) stored in a case base and by adapting their known solutions. Cases of our framework are created from text. Most of TCBR applications create cases from text by using Information Retrieval techniques, which leads to knowledge-poor descriptions of cases. We show that using semantic resources (two ontology of accidentology) makes possible to overcome this diculty, and allows us to enrich cases by using formal knowledge.
In this paper, we argue that semantic resources are likely to improve the quality of cases created from text, and, therefore, such resources can support the reasoning cycle. We illustrate this claim with our framework developed to classify documents in the accidentology domain.
