XAI-KG: Knowledge Graph to Support XAI and Decision-Making in Manufacturing

Rožanec, Jože M.; Zajec, Patrik; Kenda, Klemen; Novalija, Inna; Fortuna, Blaž; Mladenić, Dunja

doi:10.1007/978-3-030-79022-6_14

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…This research was developed for a demand forecasting use case. It extends work performed at [15], and [16], taking into account four data sources: (i) data provided by a European original equipment manufacturer targeting the global automotive industry market; (ii) news media entries provided by Event Registry [89], a well-established Media Events Retrieval System that provides real-time mainstream media monitoring; (iii) the EU Open Data Portal [90]; and (iv) the Google Knowledge Graph (Google KG) [91]. We detail the procedure in Fig.…”

Section: Use Casesupporting

confidence: 61%

“…In this research, we extend the work performed at [15] and [16]. Given a set of demand forecasts, a mapping between features and concepts that define an ontology and a hierarchy of concepts, and a set of keywords associated with each concept, we recourse to a wikification process to extract wiki concepts based on the keywords mentioned above.…”

Section: Introductionmentioning

confidence: 99%

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Enriching Artificial Intelligence Explanations with Knowledge Fragments

et al. 2022

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Artificial intelligence models are increasingly used in manufacturing to inform decision making. Responsible decision making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into the models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google knowledge graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting. The embeddings-based approach measures the similarity between relevant concepts and retrieved media news entries and datasets’ metadata based on the word movers’ distance between embeddings. The semantic-based approach recourses to wikification and measures the Jaccard distance instead. The semantic-based approach leads to more diverse entries when displaying media events and more precise and diverse results regarding recommended datasets. We conclude that the explanations provided can be further improved with information regarding the purpose of potential actions that can be taken to influence demand and to provide “what-if” analysis capabilities.

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Section: Use Casesupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Enriching Artificial Intelligence Explanations with Knowledge Fragments

et al. 2022

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“…[ 21 ] examines the implementation of a semantic content and data value chain for online direct marketing and sales in the travel sector. Two other studies by [ 52 , 53 ] present an ontology and knowledge graph in the area of manufacturing and demand forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

et al. 2023

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Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

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