Automated knowledge acquisition for second generation knowledge base systems: A conceptual analysis and taxonomy
Conference
·
OSTI ID:6455708
In this paper, we present a conceptual analysis of knowledge-base development methodologies. The purpose of this research is to help overcome the high cost and lack of efficiency in developing knowledge base representations for artificial intelligence applications. To accomplish this purpose, we analyzed the available methodologies and developed a knowledge-base development methodology taxonomy. We review manual, machine-aided, and machine-learning methodologies. A set of developed characteristics allows description and comparison among the methodologies. We present the results of this conceptual analysis of methodologies and recommendations for development of more efficient and effective tools.
- Research Organization:
- Virginia Polytechnic Inst., Blacksburg, VA (United States)
- Sponsoring Organization:
- DOE; DOD; USDOE, Washington, DC (United States); Department of Defense, Washington, DC (United States)
- DOE Contract Number:
- FG02-91NP00119
- OSTI ID:
- 6455708
- Report Number(s):
- CONF-9110444-2; ON: DE93010317; CNN: N00014-91-J-1500
- Country of Publication:
- United States
- Language:
- English
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