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Keyword Search Procedure Using Fuzzy Matching to Detect Ambiguity in Expert Formulations in Knowledge Bases of Decision Support Systems

Decision support systems use complex weakly structured system models, whose components are formulations provided by experts in a natural language. For adequate construction of models of such systems, it is important that formulations are understood the same way by different participants in group expertise, otherwise the model will not reflect the knowledge of the team of experts sufficiently correct. Because any natural language is characterized by ambiguity, measures should be taken to detect and, if possible, remove it at the stage of providing an expert formulation. For certain languages, including English, German and Ukrainian, there is a list of keywords that indicate the potential for ambiguity. Some of these keywords are variable parts of speech, so exact matching alone cannot ensure that all keywords for ambiguity detection are identified in a formulation. The use of search via fuzzy matching makes it possible to identify keywords for ambiguity detection in a non-basic form. Having tested the proposed method, the use of the search procedure in the list of keywords via fuzzy matching was able to increase the recall to maximum, which means that all paragraphs de facto containing keywords for ambiguity detection are covered using the proposed method. It has absolute precision when using keyword search to detect ambiguity, which is possible due to a known set of words that are used in search via fuzzy matching and the use of information about the part of speech and grammatical categories. The absolute precision means that no odd paragraph that does not contain keywords for ambiguity detection was covered. This increases the probability to detect text formulations that are potentially ambiguous. Since the procedure of search via fuzzy matching is much more resource-intensive than search via exact matching, the paper presents ways to increase the speed of the proposed algorithm without compromising parameters of the ambiguity detection in expert formulations.