Knowledge graph-based explainable knowledge recommendation in product development - Huang, Weifeng (Nanyang Technological University, 2021)
Product development nowadays has become more challenging than ever. Enormous heterogeneous knowledge is always involved in the process forcing the decision-making much complicated. However, most of the exiting decision-making tool like conventional knowledge recommendation system is not able to provide explicit explanation for its recommendation result, which significantly jeopardizes the persuasibility and reliability of the system. Hence, this project aims to fill the gap by establishing an explainable knowledge graph-based knowledge recommendation system by path extraction. A case study using 3D printing troubleshooting dataset is conducted to demonstrate the application of the established knowledge recommendation system in product development scenario. Moreover, two knowledge recommendation approaches: TF-IDF-based cosine similarity as the baseline and a more advanced embedding-based recommendation system (RS) called Ripplenet were conducted via the same dataset and compared with the established system in discussion section.