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| Knowledge-Discovery Method Driven by the Collaboration of Data and Knowledge: Concept, Mechanism, and Model |
| Yao Sumei1,2, Lu Quan1,2,3 |
1.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2.School of Information Management, Wuhan University, Wuhan 430072
3.Big Data Institute, Wuhan University, Wuhan 430072 |
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Abstract Knowledge discovery is a critical theoretical framework for addressing the challenges posed by vast amounts of data and complex problems, advancing scientific research and enhancing decision support capabilities. “Data” and “knowledge” are core concepts in information science, and knowledge discovery driven by data or knowledge serves as an essential approach to solving research problems in data-intensive or knowledge-intensive contexts. However, pervasive issues of imperfect data and uncertain knowledge limit the effectiveness of these methods. The co-driven approach offers an innovative pathway for discovering new knowledge through the complementary integration of data and knowledge. Despite its potential, a comprehensive and in-depth analysis of co-driven methods remains insufficient. This study adopts a cognitive logic structure of “what,” “why,” and “how” to explore the basic concepts, mechanisms, and models of knowledge discovery driven by the collaboration of data and knowledge. First, it introduces the fundamental concept of knowledge discovery through data-knowledge co-driven mechanisms, along with a detailed explanation of the newly introduced concepts of imperfect data and uncertain knowledge, which are essential components of this framework. Subsequently, the mechanism section examines the multi-path and multi-objective strategies for integrating data into knowledge-driven knowledge discovery and integrating knowledge into data-driven knowledge discovery. It explains the essence and operational mechanisms of co-driven knowledge discovery by emphasizing the cross-complementarity between data and knowledge. Finally, this study proposes a problem- and scenario-driven basic model of knowledge discovery that is co-driven by data and knowledge. It elaborates on three primary categories of internal modeling for co-driven knowledge discovery: predominantly knowledge-driven discovery (construction and error-correction modes), predominantly data-driven discovery (embedding, correction, and guidance modes), and other collaborative knowledge-discovery methods (hybrid and concurrent modes). The co-driven knowledge-discovery approach, which encompasses multiple co-driven modes, balances the complementary and synergistic effects of data and knowledge, thus providing a more comprehensive framework and process for knowledge discovery. This approach expands methodological innovation and problem-solving perspectives within the discipline of information resource management.
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Received: 12 March 2024
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