Semantic unification

This article is about semantic unification in business processes. For the concept in formal logic, see Unification (computer science).
Semantic Matching of concepts

Semantic unification, in philosophy, linguistics, and computer science, is the process of unifying lexically different concept representations that are judged to have the same semantic content (i.e., meaning). In business processes, the conceptual Semantic unification is defined as “the mapping of two expressions onto an expression in an exchange format which is equivalent to the given expression”.[1] Semantic unification has a long history in fields like philosophy and linguistics. It has been used in different research areas like grammar unification.[2][3]

Semantic unification has since been applied to the fields of business processes and workflow management. In the earliest 90’s Charles Petri at Stanford University introduced the term of semantic unification for business models, later references could be found in[4] and later formalized in Dr. Bendeck PhD thesis.[5] Petri introduced the term “Pragmatic Semantic Unification” to refer to the approaches in which the results are tested against a running application using the semantic mappings.[6] In this pragmatic approach, the accuracy of the mapping is not as important as its usability.

In general, the Semantic Unification in business processes is the process to find a common unified concept that match two lexicalized expressions into the same interpretation.


  1. Fawsy Bendeck,Automation of XML Documents Translators Generation. In 10th IEEE, International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, Cambridge, MA, Massachusetts Institute of Technology (MIT), June 2001
  2. Jonathan Calder, Mike Reape, and Hank Zeevat,, An algorithm for generation in unification categorial grammar. In Proceedings of the 4th Conference of the European Chapter of the Association for Computational Linguistics, pages 233-240, Manchester, England (10–12 April), University of Manchester Institute of Science and Technology, 1989.
  3. Graeme Hirst and David St-Onge, Lexical chains as representations of context for the detection and correction of malapropisms, 1998.
  4. Yuji Matsumoto, Hozumi Tanaka, Hideki Hirakawa, Hideo Miyoshi, and Hideki Yasukawa, BUP: a bottom-up parser embedded in Prolog. New Generation Computing, 1(2):145-158, 1983.
  5. Fawsy Bendeck, WSM-P Workflow Semantic Matching Platform, PhD Thesis, Business Computer Information System, University of Trier, Germany, 2008.
  6. Petrie, C. (2005). "Pragmatic Semantic Unification". IEEE Internet Computing. 9 (5): 96–61. doi:10.1109/MIC.2005.107.
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