A framework for an advanced XML topic map knowledge base

Today's intelligence analysts face the challenge of sifting through overwhelming amounts of disparate information in order to piece together meaningful assessments in a timely fashion. In many situations, they must respond to short deadlines, which limit their ability to conduct time consuming, traditional database or web searches. To alleviate this problem, analysts need access to a knowledge base that represents subject matter expertise, enables knowledge sharing, and is built upon a semantic standard that helps to maximize interoperability.; This dissertation presents a unique methodology and overall framework for building a standards-based multidimensional knowledge base that incorporates XML Topic Map (XTM) 1.0 as the foundation block. The Multi-layered Analytical Knowledge Organization (MAKO) Framework incorporates several key concepts and associated methodologies including the Multidimensional Ontology Model (MOM), the Temporal Layer Model (TLM), and the Assessment Scenario (AS). The MOM is a formal XTM-based framework by which multiple ontological and semantic layers are defined and integrated. This model emphasizes the integration of separate domains and enables complex queries to be constructed based upon subject matter expert knowledge. The TLM consists of a temporal ontology and semantic layer that acts as a time reference for the knowledge base. TLM offers unique methods for associating knowledge base objects to the temporal reference. By doing this, serialization of objects can be performed, thus enabling temporal inferencing, constraint management, and the ability to historically preserve knowledge base modifications as world events change. Finally, the AS is a collaboration methodology by which analysts can capture their assessments via a logical XTM-based network of topics and associations. The AS represents the analyst's assessment, which is based upon the XTM standard and is therefore easily shared via standard web based protocols.