TY - GEN
T1 - Scalable semantic annotation of text using lexical and Web resources
AU - Zavitsanos, Elias
AU - Tsatsaronis, George
AU - Varlamis, Iraklis
AU - Paliouras, Georgios
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78650434051&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12842-4_32
DO - 10.1007/978-3-642-12842-4_32
M3 - Contribución a la conferencia
AN - SCOPUS:78650434051
SN - 3642128416
SN - 9783642128417
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 287
EP - 296
BT - Artificial Intelligence
T2 - 6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications, SETN 2010
Y2 - 4 May 2010 through 7 May 2010
ER -