Matching Techniques


Fuzzy Matching Algorithms

Q. Discuss how fuzzy matching can be accomplished between objects. (Dec.99)

Ans. Fuzzy matching is accomplished by computing a fuzzy distance or similarity measure between two objects. A similarity score of 1 corresponds to an identical match while a score near 0 corresponds to a maximal dissimilarity. For example, suppose two objects say o1 and o2, are each described by the same set of k attributes Ai, I = 1, ……, k. Each attribute may be regarded as a fuzzy set. A measure of fuzzy similarity between the two objects can then be defined as

For an accurate match, the quantity d in equation should be computed for several different values of each linguistic variable.

The RETE Matching Algorithm

Q. State the basic principles of RETE matching algorithm because of which the algorithm is efficient. (Dec. 99)

Ans. The RETE matching algorithm was developed as part of the OPS family of programming languages. It was developed to eliminate the need to perform thousands of matches per cycle. This algorithm uses several novel features, including methods to avoid repetitive matching on successive cycles. The main time-saving features of RETE are as follows:

  1. In most expert systems, the contents of working memory change very little from cycle to cycle. There is a persistence in the data known as temporal redundancy. This makes exhaustive matching on every cycle unnecessary. Instead, by saving match information, it is only necessary to compare working memory changes on each cycle. In RETE, additions to, removals from, and changes to working memory are translated directly into changes to the conflict set. Then, when a rule from the conflict set has been selected to fire, it is removed from the set and the remaining entries are saved for the next cycle. Consequently, repetitive matching of all rules against Working Memory is avoided. Furthermore, by indexing rules with the condition terms appearing in their LHS, only those rules that could match Working Memory changes need to be examined. This greatly reduces the number of comparisons required on each cycle.
  2. Many rules in a knowledge base will have the same conditions occurring in their LHS. This is just another way in which unnecessary matching can arise. Repeated testing of the same conditions in those rules could be avoided by grouping rules, which share the same conditions and linking them to their common terms. It would then be possible to perform a single set of tests for all the applicable rules.
 
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