Abstract
Ontology is a representation of knowledge with a pair of concepts and relationships within a particular domain. Most of extracting techniques for non-taxonomic relation only identifies concepts and relations in a complete sentence. However, this does not represent the domain completely since there are some sentences in a domain text that have a missing or an unsure term of concepts. To overcome this issue, we propose a new algorithm based on probability theory for ontology extraction . The probability theory will be used to handle the incomplete information system , where some of the attribute values in information system are unknown or missing. The new proposed method will calculate and suggest the relevant terms, such as subject or object, that are more likely to replace the unsure value. The proposed method has been tested and evaluated with a collection of domain texts that describe tourism. Precision and recall metrics have been used to evaluate the results of the experiments. The output of this proposed method will be significantly used in the conceptualization process of the ontology engineering process to assist ontology engineers and beneficial to obtain valuable information from a variety of sources of natural language text description such as journal, structured databases of any domain, and also enable to facilitate big data analysis.
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Universiti Sains Islam Malaysia (USIM). Grant: PPP/USG-0116/FST/30/11616.
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Nabila, N.F., Basir, N., Deris, M.M. (2019). Non-taxonomic Relation Extraction Using Probability Theory. In: Ao, SI., Kim, H., Amouzegar, M. (eds) Transactions on Engineering Technologies. WCECS 2017. Springer, Singapore. https://doi.org/10.1007/978-981-13-2191-7_20
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DOI: https://doi.org/10.1007/978-981-13-2191-7_20
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