Semantic Nets are in the Eye of the Beholder

Abstract

The term semantic nets, in its broadest sense, has become virtually meaningless. It is applied to systems which, as a class, lack distinctive representational and computational properties vis a vis other knowledge representation (KR) schemes. This terminological problem is not due to lack of substance or coherence of work done under the semantic net banner. Rather, it is due to convergence of the major KR schemes: the representational and computational strategies employed in semantic net systems are abstractly equivalent to those employed in virtually all state-of-the-art systems incorporating a substantial propositional knowledge base, whether they are described as logic-based, frame-based, rule-based, or something else. In particular, I will argue that using a graph-theoretic propositional representation does not automatically distinguish it from others: even sets of PC formulas, abstractly viewed, are graphs. Nor is proximity-based inference (using graph-theoretic distance) automatically distinctive, since even resolution strategies (with reasonable indexing schemes) are proximity-based in the abstract; nor is hierarchic property inheritance any longer distinctive, given its availability in state-of-the-art logic-based, frame-based, and ruled- based systems, so I urge some more restrictive, and hence more meaningful use of the term semantic nets than is the current practice.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1990
Accession Number
ADA228441

Entities

People

  • Lenhart K. Schubert

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Automata
  • Classification
  • Computer Science
  • Computer Vision
  • Computers
  • Elephants
  • Hierarchies
  • Language
  • Machines
  • Models
  • Natural Languages
  • New York
  • Recursive Functions
  • Security
  • Semantic Models

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Educational Psychology

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms