Ebrahim Bagheri – Publication Page

Semantic Disambiguation and Linking of Quantitative Mentions in Textual Content

Mehrnaz Ghashghaei and Ebrahim Bagheri and John Cuzzola and Ali A. Ghorbani and Zeinab Noorian
Reference:
Mehrnaz Ghashghaei; Ebrahim Bagheri; John Cuzzola; Ali A. Ghorbani and Zeinab Noorian Semantic Disambiguation and Linking of Quantitative Mentions in Textual Content. In Int. J. Semantic Computing, 10 (1): 121, 2016.
Links to Publication: [doi][www][pdf]
Abstract:
Semantic annotation techniques provide the basis for linking textual content with concepts in well grounded knowledge bases. In spite of their many application areas, current semantic annotation systems have some limitations. One of the prominent limitations of such systems is that none of the existing semantic annotator systems are able to identify and disambiguate quantitative (numerical) content. In textual documents such as Web pages, specially technical contents, there are many quantitative information such as product specifications that need to be semantically qualified. In this paper, we propose an approach for annotating quantitative values in short textual content. In our approach, we identify numeric values in the text and link them to an existing property in a knowledge base. Based on this mapping, we are then able to find the concept that the property is associated with, whereby identifying both the concept and the specific property of that concept that the numeric value belongs to. Results obtained from the developed gold standard dataset show that the proposed automated semantic annotation platform is quite effective in detecting and disambiguating numerical content, and connecting them to associated properties on the external knowledge base. Our experiments show that our proposed approach is able to reach an accuracy of over 70% for semantically annotating quantitative content.
Bibtex Entry:
@article{DBLP:journals/ijsc/GhashghaeiBCGN16, author = {Mehrnaz Ghashghaei and Ebrahim Bagheri and John Cuzzola and Ali A. Ghorbani and Zeinab Noorian}, title = {Semantic Disambiguation and Linking of Quantitative Mentions in Textual Content}, journal = {Int. J. Semantic Computing}, volume = {10}, number = {1}, pages = {121}, year = {2016}, url = {http://dx.doi.org/10.1142/S1793351X16500021}, doi = {10.1142/S1793351X16500021}, timestamp = {Fri, 24 Jun 2016 14:11:10 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/ijsc/GhashghaeiBCGN16}, bibsource = {dblp computer science bibliography, http://dblp.org} webpdf = {http://ls3.rnet.ryerson.ca/papers/SEMANTIC_DISAMBIGUATION_AND_LINKING_OF_QUANTITATIVE_MENTIONS_IN_TEXTUAL_CONTENT-IJSC2016.pdf}, abstract = { Semantic annotation techniques provide the basis for linking textual content with concepts in well grounded knowledge bases. In spite of their many application areas, current semantic annotation systems have some limitations. One of the prominent limitations of such systems is that none of the existing semantic annotator systems are able to identify and disambiguate quantitative (numerical) content. In textual documents such as Web pages, specially technical contents, there are many quantitative information such as product specifications that need to be semantically qualified. In this paper, we propose an approach for annotating quantitative values in short textual content. In our approach, we identify numeric values in the text and link them to an existing property in a knowledge base. Based on this mapping, we are then able to find the concept that the property is associated with, whereby identifying both the concept and the specific property of that concept that the numeric value belongs to. Results obtained from the developed gold standard dataset show that the proposed automated semantic annotation platform is quite effective in detecting and disambiguating numerical content, and connecting them to associated properties on the external knowledge base. Our experiments show that our proposed approach is able to reach an accuracy of over 70\% for semantically annotating quantitative content.} }




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