Automatic Cause-Effect Relation Extraction from Dental Textbooks Using BERT

Chansai, Terapat; Rojpaisarnkit, Ruksit; Boriboonsub, Teerakarn; Tuarob, Suppawong; Yin, Myat Su; Haddawy, Peter; Hassan, Saeed‐Ul; Pomarlan, Mihai

doi:10.1007/978-3-030-91669-5_11

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…This provided an intuitive way of visualizing medical knowledge and could help medical personnel to quickly find the information they need. Chansai et al [29] used BERT to extract entities and relationships from dental textbooks and constructed a knowledge graph to help dental students learn the important concepts in the textbooks. The above literature demonstrates that BERT has achieved excellent performances in semantic understanding, entity recognition, and relationship extraction.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT

et al. 2023

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Knowledge graphs play an important role in the field of knowledge management by providing a simple and clear way of expressing complex data relationships. Injection molding is a highly knowledge-intensive technology, and in our previous research, we have used knowledge graphs to manage and express relevant knowledge, gradually establishing an injection molding industrial knowledge graph. However, the current way of retrieving knowledge graphs is still mainly through programming, which results in many difficulties for users without programming backgrounds when it comes to searching a graph. This study will utilize the previously established injection molding industrial knowledge graph and employ a BERT (Bidirectional Encoder Representations from Transformers) fine-tuning model to analyze the semantics of user questions. A knowledge graph will be retrieved through a search engine built on the Transformer Encoder, which can reason based on the structure of the graph to find relevant knowledge that satisfies a user’s questions. The experimental results show that both the BERT fine-tuned model and the search engine achieve an excellent performance. This approach can help engineers who do not have a knowledge graph background to retrieve information from the graph by inputting natural language queries, thereby improving the usability of the graph.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT

et al. 2023

View full text Add to dashboard Cite

show abstract

Development and Application of Knowledge Graphs for the Injection Molding Process

et al. 2023

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Injection molding, the most common method used to process plastics, is a technique with a high knowledge content; however, relevant knowledge has not been systematically organized, and as a result, there have been many bottlenecks in talent cultivation. Moreover, most of the knowledge stored in books and online articles remains in the form of unstructured data, while some even remains unwritten, resulting in many difficulties in the construction of knowledge bases. Therefore, how to extract knowledge from unstructured data and engineers’ statements is a common goal of many enterprises. This study introduced the concept of a Knowledge Graph, a triplet extraction model based on bidirectional encoder representations from transformers (BERT) which was used to extract injection molding knowledge entities from text data, as well as the relationships between such entities, which were then stored in the form of knowledge graphs after entity alignment and classification with sentence-bidirectional encoder representations from transformers. In a test, the triplet extraction model achieved an F1 score of 0.899, while the entity alignment model and the entity classification model achieved accuracies of 0.92 and 0.93, respectively. Finally, a web platform was built to integrate the functions to allow engineers to expand the knowledge graphs by inputting learning statements.

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Automatic Cause-Effect Relation Extraction from Dental Textbooks Using BERT

Cited by 2 publications

References 15 publications

Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT

Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT

Development and Application of Knowledge Graphs for the Injection Molding Process

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