Ontological semantics, formal ontology, and ambiguity

ABSTRACT

Ontological semantics is a theory of meaning in natural language and an approach to natural language processing (NLP) which uses an ontology as the central resource for extracting and representing meaning of natural language texts, reasoning about knowledge derived from texts as well as generating natural language texts based on representations of their meaning. Ontological semantics directly supports such applications as machine translation of natural languages, information extraction, text summarization, question answering, advice giving, collaborative work of networks of human and software agents, etc. Ontological semantics pays serious attention to its theoretical foundations by explicating its premises; therefore, formal ontology and its relations with ontological semantics are important. Besides a general brief discussion of these relations, the paper focuses on the important theoretical and practical issue of the distinction between ontology and natural language. It is argued that this crucial distinction lies not in the (inaccurately) presumed nonambiguity of the one and the well-established ambiguity of the other but rather in the constructed and overtly defined nature of ontological concepts and labels on which no human background knowledge can operate unintentionally to introduce ambiguity, as opposed to pervasive uncontrolled and uncontrollable ambiguity in natural language. The emphasis on this distinction, we argue, will provide better theoretical support for the central tenets of formal ontology by freeing it from the Wittgensteinian and Rortyan retreats from the analytical paradigm; it also reinforces the methodology of NLP by maintaining a productive demarcation between the language-independent nature of ontology and language-specific nature of the lexicons, a demarcation that has paid off well in consecutive implementations of ontological semantics and their applications in practical computer systems.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Theory