Abstract
Most of the existing methods focus on extracting concepts and identifying the hierarchy of concepts. However, in order to provide the whole view of the domain, the non-taxonomic relationships between concepts are also needed. Most of extracting techniques for non-taxonomic relation only identify concepts and relations in a complete sentence. However, the domain texts may not be properly presented as some sentences in domain text have missing or unsure term of concepts. This paper proposes a technique to overcome the issue of missing concepts in incomplete sentence. The proposed technique is based on the similarity precision for selecting missing concept in incomplete sentence. The approach has been tested with Science corpus. The experiment results were compared with the results that have been evaluated by the domain experts manually. The result shows that the proposed method has increased the relationships of domain texts thus providing better results compared to several existing method.
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Acknowledgements
This work was supported under Grant Universiti Sains Islam Malaysia (USIM). Grants: PPP/USG-0116/FST/30/11616.
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Heng, F.N.R., Deris, M.M., Basir, N. (2018). A Similarity Precision for Selecting Ontology Component in an Incomplete Sentence. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_10
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DOI: https://doi.org/10.1007/978-3-319-72550-5_10
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