Abstract
The availability of a web application for allocating software requirements engineering tasks to qualify personnel requires personal profile ontology (PPO) which includes both static and dynamic features. Several personal profile ontologies have been developed and deployed, but the personnel information represented is static, leaving out fundamental and dynamic properties of the personal data suitable for task handling in applications such as allocating tasks during the software requirement engineering processes. Personal profile is often modified for several purposes, calling for augmentation and annotation when needs arise. The resume is one resulting extract from personal profile and often contain slightly different information based on needs. The urgent preparation of a resume may introduce bias and incorrect information for the sole aim of projecting the personnel as being qualified for the available job. This work is aimed at providing an enhanced personal profile ontology for software requirements engineering task allocation that captures both static and dynamic properties of personal data. A mixed approach of existing ontologies like Methontology and Neon have been followed in the creation of this ontology. The enhanced personal profile ontology (e-PPO) is a constraint-based semantic data model tested using Protégé inbuilt reasoner with its updated plugins. Upon application of e-PPO, an abridged resumes otherwise referred to the smart resumes will be obtained from the populated ontology with instances, and this will aid in the decision and selection of the most qualified personnel for any queried software requirements engineering task.
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Usip, P.U., Udo, E.N., Umoeka, I.J. (2021). An Enhanced Personal Profile Ontology for Software Requirements Engineering Tasks Allocation. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_15
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