International Journal of Man-Machine Studies
Issues in the verification of knowledge in rule-based systems
International Journal of Man-Machine Studies
A comparison of similarity measures of fuzzy values
Fuzzy Sets and Systems
On certain generalizations of inner product similarity measures
Journal of the American Society for Information Science - Special issue on user-centered cooperative systems
A distance and angle similarity measure method
Journal of the American Society for Information Science
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Developing a new similarity measure from two different perspectives
Information Processing and Management: an International Journal
PREPARE: A Tool for Knowledge Base Verification
IEEE Transactions on Knowledge and Data Engineering
Bottom-Up Construction of Ontologies
IEEE Transactions on Knowledge and Data Engineering
Verification and Validation of Knowledge-Based Systems
IEEE Transactions on Knowledge and Data Engineering
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conflict resolution in a knowledge-based system using multiple attribute decision-making
Expert Systems with Applications: An International Journal
Conflict resolution in a knowledge-based system using multiple attribute decision-making
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
The rule-based knowledge based expert system has traditionally emphasized the verification of structural errors in the rule base. For conflicting or overlapping rules, designated rules are usually followed to implement prioritized or direct deletions. However, there exist no proper methods by which to resolve conflicts, inconsistencies or redundancies in values. The citation of erroneous knowledge can lead to mistakes in reaching decisions. This study proposes the conditional probability knowledge similarity algorithm and calculation system. The calculation system can quickly and accurately calculate rule-based knowledge similarity matrices and determine the conflicting or overlapping rules. Employing the group decision idea, an algorithm is provided that uses a ''reliability factor'' to refer to the reliability level of the knowledge item with a conflict, redundancy or inconsistency in value, and constructs a conflict treatment model for certainty rule-based knowledge. Most users, 94% report perplexity at the moment that conflicting or redundant rules are cited. Moreover, 92% of users hold that the algorithm is helpful to knowledge application and as an aid to the decision-making process. It can more effectively prevent mistakes in decision making and enable users to acquire more benefits from the knowledge application.