Ontology-based design information extraction and retrieval

Abstract

Because of the increasing complexity of products and the design process, as well as the popularity of computer-aided documentation tools, the number of electronic and textual design documents being generated has exploded. The availability of such extensive document resources has created new challenges and opportunities for research. These include improving design information retrieval to achieve a more coherent environment for design exploration, learning, and reuse. One critical issue is related to the construction of a structured representation for indexing design documents that record engineers' ideas and reasoning processes for a specific design. This representation should explicitly and accurately capture the important design concepts as well as the relationships between these concepts so that engineers can locate their documents of interest with less effort. For design information retrieval, we propose to use shallow natural language processing and domain-specific design ontology to automatically construct a structured and semantics-based representation from unstructured design documents. The design concepts and relationships of the representation are recognized from the document based on the identified linguistic patterns. The recognized concepts and relationships are joined to form a concept graph. The integration of these concept graphs builds an application-specific design ontology, which can be seen as the structured representation of the content of the corporate document repository, as well as an automatically populated knowledge base from previous designs. To improve the performance of design information retrieval, we have developed ontology-based query processing, where users' requests are interpreted based on their domain-specific meanings. Our approach contrasts with the traditionally used keyword-based search. An experiment to test the retrieval performance is conducted by using the design documents from a product design scenario. The results demonstrate that our method outperforms the keyword-based search techniques. This research contributes to the development and use of engineering ontology for design information retrieval.