Concept Paper for a Digital Expert: Systematic Derivation of (Causal) Bayesian Networks Based on Ontologies for Knowledge-Based Production Steps

• Published in: Machine learning and knowledge extraction Vol. 6; no. 2; pp. 898 - 916
• Main Authors: Pfaff-Kastner, Manja Mai-Ly, Wenzel, Ken, Ihlenfeldt, Steffen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Artificial intelligence; basic formal ontology (BFO); Bayesian analysis; bayesian network; causal graph; Causality; Decision making; Decision tree; digital expert; Digitization; Expected values; Graphs; Human performance; Influence; Knowledge; Knowledge bases (artificial intelligence); Knowledge management; Knowledge representation; Machine learning; Methods; Networks; Ontology; ontology-based; Resource Description Framework-RDF; Semantic web; Semantics; Web Ontology Language-OWL; Germany
• ISSN: 2504-4990; 2504-4990
• DOI: 10.3390/make6020042

Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge.