Enterprise Expert and Knowledge Discovery

Abstract

In this paper we describe two systems designed to connect users to distributed, continuously changing experts and their knowledge. Using information retrieval, information extraction, and collaborative filtering techniques, these systems are able to enhance corporate knowledge management by overcoming traditional problems of knowledge acquisition and maintenance and associated (human and financial) costs. We describe the purpose of these two systems, how they work, and current deployment in a global corporate environment to enable end users to directly discover experts and their knowledge.

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Document Details

Document Type
Technical Report
Publication Date
Aug 22, 1999
Accession Number
AD1125402

Entities

People

  • Daryl Morey
  • Dave Mattox
  • Mark Maybury

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Bayesian Networks
  • Chemical Weapons
  • Community Of Practice
  • Computer Network Security
  • Data Management
  • Data Mining
  • Databases
  • Filtration
  • Human Resources
  • Information Retrieval
  • Intelligent Agents
  • Knowledge Management
  • Lessons Learned
  • Metadata
  • Networks
  • New York
  • Probability
  • Ratings
  • Standards
  • User Interface
  • Word Processors

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Economics
  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Information Retrieval