Abstract
If we can represent the knowledge as a fully machine interpreted way, it offers many advantages to solving various kinds of problems in knowledge engineering. Most of the knowledge can be found scattered with in a domain of interest as websites, televisions, radios, publications, etc. This knowledge needs to be extracted and to be represented, so that can be used in many applications. Ontology is one of the knowledge representation techniques that is suitable for modeling domain knowledge. Knowledge evolves over time. With respect to that, we should maintain the ontology for better usage of knowledge. Ontology population is a key aspect of the ontology maintenance. However, the existing approaches for ontology populating are complex and designed for knowledge-engineering experts. Ontology Population looks for instantiating the constituent elements of an ontology. Manual population by domain experts and knowledge engineers is an expensive and time-consuming task. Thus, automatic or semi-automatic approaches are needed. The purpose of this study is to investigate in addressing the said limitation by proposing a user-friendly mechanism to incorporate evolving knowledge into ontologies, targeting ontology-illiterate end users. Maintaining ontology population and accurate inference of new knowledge are considered prime objectives of the research. A framework with flexible means of populating the ontology was developed while hiding the underlying ontology base from users. A web-based approach was adopted to support easy access and collaboratively populate. We implemented a tool based on the proposed method and checked the correctness of the method with respect to the mapping rules and all the SQL database components manually. Results proved that the proposed approach provides correct OWL-based ontology sources for the population performed through the interface. The proposed framework is designed to use any domain irrespective of the content.
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Akmeemana, R.A.O.M.P.D., Walisadeera, A.I., Goonathilake, M.D.J.S., Ginige, A. (2018). A Semi-automatic Approach to Collaboratively Populate an Ontology for Ontology-Illiterate Users. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_8
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