Abstract
In this paper we are dealing with the task of adding domain-specific semantic tags to a document, based solely on the domain ontology and generic lexical and Web resources. In this manner, we avoid the need for trained domain-specific lexical resources, which hinder the scalability of semantic annotation. More specifically, the proposed method maps the content of the document to concepts of the ontology, using the WordNet lexicon and Wikipedia. The method comprises a novel combination of measures of semantic relatedness and word sense disambiguation techniques to identify the most related ontology concepts for the document. We test the method on two case studies: (a) a set of summaries, accompanying environmental news videos, (b) a set of medical abstracts. The results in both cases show that the proposed method achieves reasonable performance, thus pointing to a promising path for scalable semantic annotation of documents.
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Zavitsanos, E., Tsatsaronis, G., Varlamis, I., Paliouras, G. (2010). Scalable Semantic Annotation of Text Using Lexical and Web Resources. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_32
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DOI: https://doi.org/10.1007/978-3-642-12842-4_32
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