Abstract
Partial matching is a comparison of two or more descriptions that identifies their similarities. Determining which of several descriptions is most similar to one description of interest is called the best match problem. Partial and best matches underlie several knowledge system functions, including: analogical reasoning, inductive inference, predicate discovery, pattern-directed inference, semantic interpretation, and speech and image understanding. Because partial matching is both combinatorial and ill-structured, admissible algorithms are elusive. Economical solutions require very effective use of constraints that, apparently, can be provided only by globally organized knowledge bases. Examples of such organizations are provided, and promising avenues of research are proposed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.