Ph.D. Dissertation Defense by Nsikan J. Udoyen
Monday, November 29, 2004

(Dr. David Rosen, Chair)

"Information Modeling for Intent Based Characterization and Retrieval of Finite Element Analysis Models"


Archived finite element analysis (FEA) models are a source of information often referred to in the development of new models. Automated retrieval of these models from electronic repositories presents a unique information retrieval scenario because they are supported by information in other formats which must be pieced together to explain its content. These range from spatial representations such as CAD models, sketches and diagrams, to natural language accounts such as notes, reports and annotations in models scripts. Basic search techniques for electronic repositories rely on superficial document attributes and keywords. Such queries are inadequate for open, dynamic repositories, accessible to more users and applications and constantly being expanded with new documents without the restrictions on format that would exist in a database or knowledgebase. The low precision and recall associated with such searches translates into time spent opening files to manually assess their relevance. A more precise means of describing the information sought is required that enables the comparison of intent, content and context to determine relevance.

The aim of the proposed research is to facilitate automated retrieval of relevant FEA models and related information from open electronic repositories through the development of formal taxonomies that enable the representation of document collections as relational data structures. A grammar-based approach with its foundations in Formal Concept Analysis and the Description Logic ALE is proposed to enable the creation of these representations, modeling of the information involved and implementation of queries to find analogous models. The use of structural subsumption, least common subsumer computation and concept lattices to assess similarities between representations to support retrieval of analogous models and supporting documents will be explored. The impact of their use to incorporate semantics into queries on recall and query precision will be assessed. Computationally feasible approaches to implementing these queries will be developed for FEA model retrieval. The approach will be demonstrated using FEA models of electronic components and supporting documents stored in electronic repositories.