Knowledge Loss Measurement through Quality Evaluation of Conceptual Graphs
Publication: Computing in Civil Engineering 2021
ABSTRACT
Conceptual graph (CG) is a graph representation of logic derived from semantic knowledge. In the rule interpretation process in automated compliance checking (ACC), the quality of knowledge exchange between rule experts and rule implementers is difficult to ensure. CG is an effective communication tool to address this issue. However, quantifying the amount of knowledge transferred through CG is challenging considering the subjectiveness existing in people’s understanding of rules. This paper proposes an assessment framework including both subjective and objective criteria of a CG to measure its quality by comparing the knowledge captured by the CG and the knowledge in the original rules. Four groups of criteria are studied: correctness, completeness, complexity, and comprehensibility. The framework is validated through a case study using a building code related to gross floor area (GFA). The CG generated by the rule implementer is evaluated to test the rule implementer’s knowledge. The evaluation result shows that the rule implementer can capture most of the knowledge in the original building code. The mistakes mostly exist in notations of the CG, which could be due to his/her unfamiliarity with it. Overall, the CG can be easily implemented to represent the building code and check rule implementers’ knowledge.
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REFERENCES
Beach, T. H., Kasim, T., Li, H., Nisbet, N., and Rezgui, Y. (2013). Towards automated compliance checking in the construction industry. Springer, 366–380.
El-Diraby Tamer, E. (2013). “Domain Ontology for Construction Knowledge.” Journal of Construction Engineering and Management, 139(7), 768–784.
Fazio, P., Hammad, A., and Tan, X. (2010). “Automated Code Compliance Checking for Building Envelope Design.” Journal of Computing in Civil Engineering, 24(2), 203–211.
Lee, H., Kim, J., Shin, M., Kim, I., and Lee, J.-K. (2014). A demonstration of BIM-enabled quantitative circulation analysis using BERA language. IAARC Publications, 1.
Malsane, S., Matthews, J., Lockley, S., Love, P. E. D., and Greenwood, D. (2015). “Development of an object model for automated compliance checking.” Automation in Construction, 49, 51–58.
Pauwels, P., Van Deursen, D., Verstraeten, R., De Roo, J., De Meyer, R., Van de Walle, R., and Van Campenhout, J. (2011). “A semantic rule checking environment for building performance checking.” Automation in Construction, 20(5), 506–518.
Solihin, W., Dimyadi, J., and Lee, Y.-C. (2019). “In search of open and practical language-driven BIM-based automated rule checking systems.” Advances in informatics and computing in civil and construction engineering, Springer, 577–584.
Solihin, W., and Eastman, C. M. (2016). “A knowledge representation approach in BIM rule requirement analysis using the conceptual graph.” ITcon, 21, 370–401.
Sowa, J. F. (1984). Conceptual structures: information processing in mind and machine. The Systems programming series, Addison-Wesley, Reading, Mass.
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Computing in Civil Engineering 2021
Pages: 506 - 513
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Published online: May 24, 2022
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