Manufacturing Knowledge Graph: A Connectivism to Answer Production Problems Query With Knowledge Reuse

Manufacturing knowledge (MK) is enjoying a "new golden age" in the academic domain, marked by vast reuse to support product-related production problems (PPs) solving decision making for manufacturing enterprises in the industry sector. However, the practice of MK reuse and research is fragmented and insufficient, which cannot be mature to provide a systemic solution for that a decision-maker has to consider the involving issues: how MK can be used earlier and rightly; what kind of practical problems can be solved? In order to answer those interconnecting issues, this paper firstly proposes a connectivism framework to clarify the compressive relationship of problem-to-problem, knowledge-to-knowledge and problem-to-knowledge with knowledge integration, knowledge matching, and problem-solving layers. Then, based on the framework, an ontology-based MK graph (MKG) is constructed with a unified MK-filter to collect and integrate multifactor and multilevel MK, and a graph-oriented meta-knowledge model (MKM) is proposed to represent the details between the knowledge entities (i.e., concept and instance), which also shows the contribution to knowledge reasoning. After that, driven by a structure temporal query (i.e., 5W2H), a semantics-based knowledge computation is developed to compute the intrinsic term similarity (IS) and relational term similarity (RS) between two knowledge entities in the MKG. Finally, a case study is taken to demonstrate the effectiveness and performance of the proposed methods.