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As the demands for large-scale information processing have grown, knowledgegraph-based approaches have gained prominence for representing general anddomain knowledge. The development of such general representations is essential,particularly in domains such as manufacturing which intelligent processes andadaptive education can enhance. Despite the continuous accumulation of text inthese domains, the lack of structured data has created information extractionand knowledge transfer barriers. In this paper, we report on work towardsdeveloping robust knowledge graphs based upon entity and relation data for bothcommercial and educational uses. To create the FabKG (Manufacturing knowledgegraph), 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 leveragingstudent notes, which contain invaluable information but are not captured asmeaningful information, excluding their use in personal preparation forlearning and written exams. We have created a knowledge graph containing 65000+triples using all data sources. We have also shown the use case ofdomain-specific question answering and expression/formula-based questionanswering for educational purposes.

FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source