Semantical Machine Understanding
Abstract
Semantical Machine Understanding is the foundation for automatic sense and decision making of multinational, multicultural, and coalition applications. We show an innovative semantical machine understanding system that can be installed on each node of a network and used as a semantic search engine. Innovations of such a system include 1" text mining: extract concepts and meaning clusters based on contexts using pattern recognition and machine learning; 2" meaning learning: extract knowledge patterns that link human labeled meaning to raw data. The knowledge patterns can be applied to predict future data; and 3" collaborative meaning search: incorporate humans and machines to form a collaborative network to search and enhance the meaning iteratively. In this paper, we also show the feasibility of using a semantic search architecture and discuss the two ways it is drastically different from current search engines: 1" indexes embedded in agents are distributed and customized to the learning and knowledge patterns of their own environment and culture. This allows data providers to maintain their own data in their own environment, but still share indexes across peers; 2" Semantic machine understanding enables discovery of new information rather than popular information.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2008
- Accession Number
- ADA486897
Entities
People
- Charles C. Zhou
- Chetan Kotak
- Ying Zhao