Knowledge Representation: A Conceptual Modeling Approach

Knowledge Representation: A Conceptual Modeling Approach

Cecil Eng Huang Chua (University of Auckland, New Zealand), Veda C. Storey (Georgia State University, USA) and Roger H. Chiang (University of Cincinnati, USA)
Copyright: © 2012 |Pages: 30
DOI: 10.4018/jdm.2012010101
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Substantial work in knowledge engineering has focused on eliciting knowledge and representing it in a computational form. However, before elicited knowledge can be represented, it must be integrated and transformed so the knowledge engineer can understand it. This research identifies the need to separate knowledge representation into human comprehension and computational reasoning and shows that this will lead to better knowledge representation. Modeling of human comprehension is called conceptual knowledge representation. The Conceptual Knowledge Representation Scheme is developed and validated by conducting a combined qualitative/quantitative repeated-measures experiment comparing the Conceptual Knowledge Representation Scheme to two computation-oriented ones. The results demonstrate that the Conceptual Knowledge Representation Scheme better facilitates human comprehension than existing representation schemes. Four principles of the Conceptual Knowledge Representation Scheme emerge that help to attain effective knowledge representation. These are: (1) a focus on human comprehension only, (2) design around natural language, (3) addition of constructs common in the domain, and (4) constructs for representing abstract versions of detailed concepts.
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Knowledge engineering is an important part of capturing and representing knowledge about the real world so it can be used in many different types of information systems – from regular software systems that support business applications to artificial intelligent applications. However, knowledge engineering has remained manual and difficult due, in part, to the basic problems of understanding the real world, capturing the semantics of the real world, and representing concepts of the real world in a form that humans can understand. Because knowledge engineering is so challenging, the quality of knowledge captured and represented is often less than optimal (Davis, Schrobe, & Szolovits, 1993).

One reason knowledge representation remains a fundamental challenge in knowledge engineering (Grigorova & Nikolov, 2007; Holder, Markov, & Russell, 2006; Leone et al., 2006) is because tools for knowledge representation attempt to address multiple issues simultaneously. Knowledge representation tools normally perform two tasks. First, they model the domain knowledge of human experts. Second, they are a representation of the implementation of domain knowledge in the knowledge system (Davis et al., 1993). As a result, features are incorporated into tools that make trade-offs between these two issues. For example, conceptual graphs were intended to facilitate human understanding (Sowa, 1984). However, elements of conceptual graphs make it difficult for humans to accurately model human knowledge. As one example, conceptual graphs include a construct called a conceptual relation that has a computational, but not a human, analog.

To illustrate, consider the classic Conceptual Graphs example “John is going to Boston by Bus” shown in Figure 1. Agent (agnt), destination (dest) and instrument (inst) are conceptual relations created to define the notion of “go.” The fact that “go” requires an agent, destination and instrument is important for a computer to understand. Human beings, however, would consider this way of modeling “go” unnatural.

Figure 1.

Conceptual Graph- John is going to Boston by Bus (From SOWA, Knowledge Representation, 1E, © 2000 South-Western, a part of Cengage Learning, Inc. Reproduced by permission.


Conceptual modeling research has long argued that modeling what the user wants (i.e., conceptual modeling) should be distinct from designing the implementation artifact (i.e., logical design) (Langefors, 1995; Navathe, 1992). Given that knowledge representation appears to do both, knowledge representation should be improved by separating the work into two tasks, and developing tools (i.e., representation schemes) that maximize performance for each task. Specifically, knowledge representation schemes currently focus both on modeling the knowledge domain and representing it in a computational form. Features of existing schemes therefore, simultaneously: (1) represent captured knowledge in a human comprehensible format to facilitate communication with domain experts (i.e., conceptual knowledge representation), and (2) transform captured knowledge into a computational form for reasoning by computers (i.e., computational reasoning). We argue that knowledge representation would be made more effective if separate representation schemes were developed for each task, instead of having a single scheme perform both tasks.

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