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

TL;DR

FabKG is a comprehensive manufacturing knowledge graph built from structured and unstructured sources, enabling improved information extraction, knowledge transfer, and educational question answering in the manufacturing domain.

Contribution

The paper introduces FabKG, a novel manufacturing knowledge graph created using diverse data sources and a crowdsourcing method leveraging student notes, enhancing domain knowledge representation.

Findings

Created a knowledge graph with 65,000+ triples from multiple sources.

Demonstrated domain-specific question answering capabilities.

Showcased educational applications with formula-based question answering.

Abstract

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.