A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned during the Microchip Design
This addresses information retrieval challenges for engineers in microchip design, though it is incremental as it applies existing text mining methods to a specific domain.
The paper tackles the problem of overwhelming textual data in microchip production by proposing a dynamic knowledge graph approach to interlink information from multisource documents, enhancing searchability and access to design-failure-cases during new chip design.
The production of microchips is a complex and thus well documented process. Therefore, available textual data about the production can be overwhelming in terms of quantity. This affects the visibility and retrieval of a certain piece of information when it is most needed. In this paper, we propose a dynamic approach to interlink the information extracted from multisource production-relevant documents through the creation of a knowledge graph. This graph is constructed in order to support searchability and enhance user's access to large-scale production information. Text mining methods are firstly utilized to extract data from multiple documentation sources. Document relations are then mined and extracted for the composition of the knowledge graph. Graph search functionality is then supported with a recommendation use-case to enhance users' access to information that is related to the initial documents. The proposed approach is tailored to and tested on microchip design-relevant documents. It enhances the visibility and findability of previous design-failure-cases during the process of a new chip design.