Validating the Knowledge Acquisition Process Metrics in Content Management Systems
Jason Jeya Singh1, Justus S2
1Jason Jeya Singh, Print Pura Technologies, Charlotte, North Carolina, USA.
2Justus S, Associate Professor, Department of Computing Science and Engineering, VIT University, Chennai (Tamil Nadu), India.
Manuscript received on 16 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 257-262 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10491291S319/19©BEIESP | DOI: 10.35940/ijeat.A1049.1291S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Measuring the processes involved in knowledge engineering for designing and building an intelligent system has taken significant role. Out of the four basic processes involved in knowledge engineering, this paper deals with the knowledge acquisition process and the metrics necessary for measuring the process itself. Three metrics are proposed for the knowledge acquisition process based on the entailment procedures, its length and complexity, and the cohesion and coupling attributes of the collection of knowledge units. These three metrics are formalized based on the Briand’s mathematical properties for validating software metrics. These metrics are indicative in the way it gives an insight on the design and the development of a knowledgebase. In addition to these metrics, newer metrics can also be proposed for knowledge representation and knowledge sharing processes.
Keywords: Knowledge Engineering, Knowledge Acquisition, Software Metrics, Metrics Validation.
Scope of the Article: Process & Device Technologies