FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source
This work addresses information extraction and knowledge transfer barriers in manufacturing for commercial and educational uses, though it is incremental as it builds on existing knowledge graph methods with new data sources.
The authors tackled the lack of structured data in manufacturing by developing FabKG, a knowledge graph with over 65,000 triples, using sources like textbooks, research papers, and a novel crowdsourcing method from student notes, and demonstrated its application in domain-specific question answering.
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.