Similarity Measurement of Rule-based Knowledge using Conditional Probability

This study proposes the RO-RA-RV structure for rule-based knowledge and integrates methods of conditional probability, vector matrices, and artificial intelligence to establish a conditional probability knowledge similarity algorithm and calculation system. This calculation system can quickly and accurately calculate rule-based knowledge similarity matrices and determine the relationship among knowledge items, this relationship can function as the knowledge source for treatments that increase the addedvalue of the knowledge, through the inference of value-added treatment such as merging, integration, deletion, innovation and additions, the accuracy of the knowledge itself can be securely ensured and wrong decisions be avoided. According to the knowledge similarity matrices, the knowledge case most similar to the testing case can be quickly retrieved, and used for all types of with Knowledge-Based Reasoning or Case-Based Reasoning to help decision making and prediction.

[1]  Wei-Tek Tsai,et al.  Verification and Validation of Knowledge-Based Systems , 1999, IEEE Trans. Knowl. Data Eng..

[2]  Robert R. Korfhage,et al.  A distance and angle similarity measure method , 1999 .

[3]  Malek Adjouadi,et al.  A similarity measure for stereo feature matching , 1997, IEEE Trans. Image Process..

[4]  Chen-Tung Chen,et al.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000, Fuzzy Sets Syst..

[5]  Tian-ming Wang,et al.  A relative similarity measure for the similarity analysis of DNA sequences , 2005 .

[6]  Du Zhang,et al.  A TOOL FOR KNOWLEDGE BASE VERIFICATION , 1993 .

[7]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[8]  H. Chertkow,et al.  Semantic memory , 2002, Current neurology and neuroscience reports.

[9]  Miin-Shen Yang,et al.  A similarity-based robust clustering method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[11]  Christopher J. R. Green,et al.  Verification and validation of expert systems , 1991 .

[12]  Jin Zhang,et al.  Developing a new similarity measure from two different perspectives , 2001, Inf. Process. Manag..

[13]  Shyi-Ming Chen,et al.  A comparison of similarity measures of fuzzy values , 1995 .

[14]  Qi Liu,et al.  A new similarity measure of generalized fuzzy numbers and its application to pattern recognition , 2004, Pattern Recognit. Lett..

[15]  Ying-Hong Lin,et al.  Rule-based Knowledge Similarity Distance Calculation System-Using Fuzzy Conditional Probability Knowledge Similarity Algorithm , 2005, DMS.

[16]  Ying-Hong Lin,et al.  A New Approach for Rule-Based Knowledge Value-Added Treatment Inference , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[17]  Min-Yuan Cheng,et al.  Using Conditional Probability to Measure Rule-based Knowledge Similarity , 2006, SEKE.

[18]  Bernd-Jürgen Falkowski.  On Certain Generalizations of Inner Product Similarity Measures , 1998, J. Am. Soc. Inf. Sci..

[19]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[20]  Nicolaas J. I. Mars,et al.  Bottom-Up Construction of Ontologies , 1998, IEEE Trans. Knowl. Data Eng..