2020/08/24 by H. Abu-Rasheed, Hasan Abu Rasheed, C. Weber +9 · 5 citations
Computer Science · Engineering · #Advanced Graph Neural Networks #Composition (language) #Documentation #Graph #Graph Theory and Algorithms #Knowledge graph #Process (computing) #Production (economics) #VLSI and FPGA Design Techniques #Visibility #cs.IR
paper · pdf · doi:10.1007/978-3-030-55187-2_43
published in Advances in intelligent systems and computing, 594-610 (Springer Nature)
openalex publication_date 2020/08/24 · openalex created_date 2020/09/01 · arxiv created 2021/05/11 · arxiv updated 2021/05/12 · openalex updated_date 2026/08/05
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.