Currently, the numerical simulation of flow and/or water quality becomes more and more sophisticated. There arises a demand on the integration of recent knowledge management (KM), artificial intelligence technology with the conventional hydraulic algorithmic models in order to assist novice application users in selection and manipulation of various mathematical tools. In this paper, an ontology-based KM system (KMS) is presented, which employs a three-stage life cycle for the ontology design and a Java/XML-based scheme for automatically generating knowledge search components. The prototype KMS on flow and water quality is addressed to simulate human expertise during the problem solving by incorporating artificial intelligence and coupling various descriptive knowledge, procedural knowledge and reasoning knowledge involved in the coastal hydraulic and transport processes. The ontology is divided into information ontology and domain ontology in order to realize the objective of semantic match for knowledge search. The architecture, the development and the implementation of the prototype system are described in details. Both forward chaining and backward chaining are used collectively during the inference process. In order to demonstrate the application of the prototype KMS, a case study is presented. Keywords: Knowledge management system; flow and water quality modeling; artificial intelligence; ontology-based The current techniques for numerical simulation of flow and/or water quality are highly specialized tasks. The numerical technique can be based on finite element method, finite difference method, boundary element method and Eulerian-Lagrangian method. The time-stepping algorithm can be implicit, explicit or characteristic-based. The shape function can be of first order, second order or higher order. The modeling can be simplified into different spatial dimensions, i.e., 1-dimensional model, 2-dimensional depth-averaged model, 2-dimensional layered model, 3-dimensional model, and so on (Blumberg et al., 1999; Chau et al., 1996; Chau and Jin, 1998; Tucciarelli and Termini, 2000) . Heuristics, empirical experience of specialists, simplifications and modeling techniques are included in the analysis of coastal hydraulics and water quality (Yu and Righetto, 2001) . The accuracy of the prediction depends largely on the accuracy of the open boundary conditions, model parameters used, and the numerical scheme adopted (Martin et al., 1999) . It is generally recognized that the most important assets are the expertise knowledge. The sources of knowledge are not only from books, technical manuals, and education trainings, but also the accumulation of long-term experience which is usually stored in written documents. Since the diversity and complexity of conceptual terminology in the industry and the lack of proper document management, the existing knowledge is hard to be systematically arranged and reserved, even shared, and engineers have to spend many efforts in searching the knowledge they need. As a result, it is desirable to establish the bridge between model developers and application users. The application of knowledge management (KM) is considered a feasible
Chau REF presented an ontology-based knowledge management system (KMS) to enable novice users to find numerical flow and water quality models given a set of constraints.
An ontology-based knowledge management system for flow and water quality modeling
Adv. Eng. Softw.
Adv. Eng. Softw.
Computer Science