F ab KG : A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source (2022)

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.

## introduction in recent years, the advancement of artificial intelligence applications has grown multifold. many areas such as natural language processing, digital twins @xcite , and chatbots @xcite have become very popular for their ability to record and use information from unstructured sources efficiently. one such application is knowledge graph (kg), which has gained popularity in various domains due to its potential applications. a knowledge graph is a graph meant to accumulate and impart real-world knowledge, with nodes rep-resenting entities of interest and edges representing potentially diverse relations between the entities. a kg has varied applications in recommendations, search, question answering and many more. most importantly, a kg can be used to make decisions based on inferences. the use of a knowledge graph is of high value in making design and manufacturing-related decisions. as there has been an explosion of knowledge addition in various design considerations and manufacturing decisions, most of the knowledge is with small and medium-sized enterprises (smes). the decision-making in design and production could be significantly improved using knowledge graphs @xcite . it can benefit not only small and medium manufacturers @xcite , but also hardware-based entrepreneurs and help boost self-sustaining product development @xcite . a number of prior researchers have started developing manufacturing related knowledge graphs based on specific problem areas such as machining process planning @xcite , workshop resource kg @xcite , intelligent manufacturing @xcite , faults @xcite , maintenance @xcite and industry 4.0 @xcite @xcite . however, none of these graphs represent fundamental knowledge of manufacturing concepts, processes, process parameters, characterization, materials, applications, and various other basic aspects of manufacturing domain education. a large amount of such fragmented knowledge can be integrated to assist the learners in intuitively and easily connecting with the knowledge system by leveraging the nodes and relationships. such knowledge integration will also assist in intelligent question answering that can accelerate knowledge discovery and search. google bases part of its knowledge vault on the well-known wikidata knowledge base @xcite . even though wikidata has a large amount of information from wikipedia, there is a dearth of standardized knowledge regarding many important entities related to the manufacturing domain. for instance, the term 'additive manufacturing' is present as '3d printing'; while there have been substantial developments in the field of 'metal additive manufacturing' (metal am) over the last decade, it is not present as a subclass of '3d printing' in wikidata. moreover, within metal additive manufacturing (frazier, 2014), sub-classifications such as dmls, ebam, and pbf are not present in wikidata. one reason for this is the volunteerdriven nature of wikidata as a knowledge base; this has led to a limited amount of specialist terminology and information regarding the manufacturing domain. therefore, wikidata cannot provide direct answers to questions that are very specific to this domain. to understand the basic concepts in the context of manufacturing we focus on formulating answers to some basic questions such as, 'what are some precision finishing manufacturing process?', 'what are some tools for machining copper?' etc. the purpose of creating such a knowledge graph of manufacturing using wikidata is to provide a starting point for a structured manufacturing knowledge base, which can be amalgamated with knowledge from other sources such as textbook @xcite and research articles @xcite . to tackle the challenges in developing the knowledge graph from scratch for manufacturing science, we consider various methodologies for creating accurate graphs. we propose a merged knowledge graph that combines the existing structured wikidata knowledge graph with a novel semi-supervised knowledge graph extracted from textbook data. for extracting graph triples from wikidata, as mentioned in figure 1 , we have adopted two methods for the approach: (1) vocabulary-based and (2) based on unstructured text. former includes fetching wikidata items using a collection of manufacturing vocabulary terms through the utilization of textbook index words, keywords from research papers, and named entity recognition using fabner @xcite , followed by the use of dbpedia @xcite to find wikidata items. the latter is a semi-supervised approach that utilizes students' notes, considering standard textbooks as the reference. the most significant purpose of the latter method is to make use of textbook knowledge structured by humans, thereby increasing the quality of the knowledge base. the following sections elaborate on the details of the methodology and implementation. ## conclusion and future work we have developed fabkg -a knowledge graph for product design and manufacturing, which utilizes two critical sources of knowledge, (1) wikidata and (2) human constructed notes, that combine structured/unstructured knowledge towards answering question-related to product development and manufacturing. using this kg, students, product developers, and knowledge seekers can get good insights into various concepts and fundamentals about various topics in this domain. using all the methods described above, we have found 65000+ triples in 12 entity categories. in the future, we plan to use the heterogeneous knowledge graph for directed relation prediction in the bigger corpus, performing graph embedding and link prediction. moreover, lecture presentations with succinct text could also be utilized for finding entities and relations. generally, the title/topic of the presentation symbolizes the subject, with some entities either written directly or placed after another subtopic. furthermore, 'property/attribute' of relation through the specific value of entities such as the strength of materials, carbon content, brinell hardness, etc., currently available in tabular form in books and other resources, can be added to the kg. the same could be represented using a hypergraph by combining multimodal data. therefore, the new graph structure would have not only an 'entity-relation-entity' type graph but also an 'entity-attribute-value' graph. finally, this knowledge graph could help link to global knowledge by contributing to existing wikidata knowledge with the help of wikimapper.