The Case for Using Semantic Nets as a Convergence Format for Symbolic Information Fusion

Abstract

People have to deal with an impressive continuum of representations, from fully numeric and structured to totally textual and unstructured. Solving this situation of heterogeneity is a prerequisite to information fusion processes and algorithms. However, in the human brain, the distinction between structured and unstructured data simply does not exist. Humans are easily able to merge information coming from heterogeneous sources. How can computers mimic this extraordinary capability? The solution is to represent information in machines in a way that is similar to the way it is represented in the human brain. This subject was studied for years in the field of Artificial Intelligence, and, as early as the 1950s, the concept of Semantic Nets arrived to meet this challenge. Semantic Nets are an extremely efficient and human friendly way of representing complex information. The authors started developing and using a tool dedicated to the management of Semantic Nets in the early 1990s. Their first experiments with Ideliance showed that Semantic Nets can play an important role in Symbolic Intelligence Fusion. Transforming and merging heterogeneous information from various formats (databases, tables, messages, texts) into a unique format (what they call Format Fusion) is a good basis for Intelligence Fusion. First, it offers an efficient support for "manual" seamless inspection and navigation of the whole set of information. Second, it becomes a material upon which powerful data analysis (distance and cluster computation) can be performed. They call this process "Litteratus Calculus." Their conjecture is that the objects resulting from this analysis form the backbone of the Intelligence Fusion process, which, ultimately, is the domain of human decision. Twenty-two briefing charts summarize the presentation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA428706

Entities

People

  • Jean Rohmer

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Automatic
  • Computations
  • Computer Programming
  • Computer Science
  • Data Analysis
  • Databases
  • Domain Specific Programming Languages
  • Information Science
  • Information Systems
  • Language
  • Models
  • Natural Languages
  • Ontologies
  • Relational Databases
  • Text Mining

Readers

  • Business Analytics
  • Educational Psychology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy