MAKO: Multi-Ontology Analytical Knowledge Organization based on Topic Maps

Authors:
A. Riki Y. Morikawa;Larry Kerschberg
Affiliations:
George Mason University, Fairfax, Virginia;George Mason University, Fairfax, Virginia
Venue:
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Year:
2004

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Abstract

This paper addresses how the XML Topic Map (XTM) 1.0 standard can be used to develop an analytical knowledge base comprised of multiple ontologies to support intelligence assessments. Termed the Multi-Ontology Analytical Knowledge Organizational (MAKO) framework, it incorporates a Multidimensional Ontology Model (MOM) that organizes subjects into separate conceptualizations based upon common-sense groupings. Topic, association and occurrence elements are temporally serialized, according to the Temporal Layer Model (TLM), to accommodate, and historically preserve, modifications to the knowledge base as world events change.