CBKR+: A Conceptual Framework for Improving Corpus Based Knowledge Representation
As contributions of the CBKR+ framework, the researcher (1) defined a new domain categorization model called Basis for Categorization model, (2) incorporated the Basis for Categorization model to (a) facilitate a first level categorization of the schema components in the corpus, and (b) define the Set of Criteria for Association to cover all types of associations and association agents, (3) defined analysis mechanisms to identify and extract further associations in the corpus in the form of the Set of Criteria for Association, and (4) improved the expressiveness of the representation, and made it suitable for hypothesis testing and prediction in context using the above.
The application of the framework was demonstrated, first, by using it on examples from the CBKR methodology, and second, by applying it on 12 domain representations acquired from multiple sources from the physical-world domain of Criminology. The researcher arrived at the conclusion that the proposed CBKR+ framework provided an organized approach that was more expressive, and supported deeper analyses through more diagnostic and probability-based forms of queries.
School:University of Waterloo
School Location:Canada - Ontario
Source Type:Master's Thesis
Keywords:computer science corpus based knowledge representation
Date of Publication:01/01/2006