Abstract
This paper proposes a method of using ontology hierarchy in automatic topic identification. The fundamental idea behind this work is to exploit an ontology hierarchical structure in order to find a topic of a text. The keywords that are extracted from a given text will be mapped onto their corresponding concepts in the ontology. By optimizing the corresponding concepts, we will pick a single node among the concepts nodes that we believe is the topic of the target text. However, a limited vocabulary problem is encountered while mapping the keywords onto their corresponding concepts. This situation forces us to extend the ontology by enriching each of its concepts with new concepts using the external linguistics knowledge-base (WordNet). Our intuition of a high number keywords mapped onto the ontology concepts is that our topic identification technique can perform at its best.
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Tiun, S., Abdullah, R., Kong, T.E. (2001). Automatic Topic Identification Using Ontology Hierarchy. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2001. Lecture Notes in Computer Science, vol 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44686-9_43
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DOI: https://doi.org/10.1007/3-540-44686-9_43
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