As the demands for large-scale information processing have grown, knowledge
graph-based approaches have gained prominence for representing general and
domain knowledge. The development of such general representations is essential,
particularly in domains such as manufacturing which intelligent processes and
adaptive education can enhance. Despite the continuous accumulation of text in
these domains, the lack of structured data has created information extraction
and knowledge transfer barriers. In this paper, we report on work towards
developing robust knowledge graphs based upon entity and relation data for both
commercial and educational uses. To create the FabKG (Manufacturing knowledge
graph), we have utilized textbook index words, research paper keywords, FabNER
(manufacturing NER), to extract a sub knowledge base contained within Wikidata.
Moreover, we propose a novel crowdsourcing method for KG creation by leveraging
student notes, which contain invaluable information but are not captured as
meaningful information, excluding their use in personal preparation for
learning and written exams. We have created a knowledge graph containing 65000+
triples using all data sources. We have also shown the use case of
domain-specific question answering and expression/formula-based question
answering for educational purposes.

本文介绍了利用基于实体和关系的数据构建知识图谱的方法以及将众包方法应用于知识图谱构建，在制造业领域中建立了一个包含 65000 + 三元组的 FabKG，并展示了区分领域特定问题和基于表达式 / 公式的问题的用例。