A relationship extraction method for domain knowledge graph construction

Yu, Haoze; Li, Haisheng; Mao, Dianhui; Cai, Qiang

doi:10.1007/s11280-019-00765-y

Cited by 56 publications

(17 citation statements)

References 22 publications

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“…Information extraction technology can be applied to many fields, such as commodity search [37], text mining [10], biology [28], medical treatment [1], [13] and so on. Information extraction mainly includes named entity recognition [2], [24], relationship extraction [36], attribute extraction [23] and other tasks. In this paper, we study the problem of extracting attribute values from the unstructured text in electronic medical records.…”

Section: Related Workmentioning

confidence: 99%

Information Extraction for Intestinal Cancer Electronic Medical Records

et al. 2020

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The data generated by the structured electronic medical records is helpful for mining and extracting medical data, and it is an effective way to make effective use of valuable data resources. However, the hospitals have accumulated a large number of unstructured data in electronic medical records, which cannot be effectively searched, resulting in serious waste of resources. In this paper, we study the problem of extracting attribute values from the unstructured text in electronic medical records. By observing intestinal cancer diagnostic texts, our attributes have two categories-discriminative attributes and extractive attributes, which use the text classification and the sequence labeling to tackle attribute values extraction problems. For discriminative attributes, we firstly divide the text into sentences/segments as instances. Secondly, we finetune the pre-trained word embedding to capture domain-specific semantics/knowledge. Thirdly, we also use an attention mechanism to select the most important instance for different attribute extractors. Finally, multi-tasking learning is used to share useful information to get better experimental results. For extractive attributes, we propose a novel model to get attribute values, including the BiLSTM layer, the CNN layer and the CRF layer. In particular, we use BiLSTM and CNN to learn text features and CRF as the last layer of the model. Experiments have shown that our method is superior to several competitive baseline methods.

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Section: Related Workmentioning

confidence: 99%

Information Extraction for Intestinal Cancer Electronic Medical Records

et al. 2020

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“…As a semantic knowledge base, knowledge graph is a powerful tool for managing large-scale knowledge consists with entities and relations between them. Haoze Yu proposed a relation extraction method for the construction of knowledge graph in food field [3].…”

Section: Related Workmentioning

confidence: 99%

Distant Supervision for Relations Extraction via Deep Residual Learning and Multi-instance Attention in Cybersecurity

Shen

Qin

Wang

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A large number of open source threat intelligence resources provide regularly updated threat sources that can be applied to a variety of security analysis solutions. Fragmented security news, security forums, and vulnerability information are important sources of cyber threat intelligence, but it is difficult to correlate these multiple-source data. Cybersecurity knowledge graph is a powerful tool for data-driven thread intelligence computing. Relation extraction is a very important task in construction of cybersecurity knowledge graph from unstructured data. In order to reduce the influence of noisy data in deep learning model, we propose a distant supervised relation extraction model ResPCNN-ATT based on deep residual convolutional neural network and attention mechanism. This method takes word vector and position vector of the word as input of the model, extracts semantic features of texts through the piecewise convolutional neural network model PCNN, achieves the learning effect of less noisy data and better extracts deep semantic features in sentenses by using deep residuals Compared with other models, the model proposed in this paper achieves higher accuracy than other models.

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“…This article introduces the adjustment factor P s shown in equation (11) on the basis of the initial function, which would be infinitely close to 1 when the prediction value is close to the truth and approach 0 when the relationship is misclassified. The cross-entropy loss function with the adjustment factor is shown in equation (12), where N represents the quantity of sentences…”

Section: Improved Cross-entropy Loss Functionmentioning

confidence: 99%

“…Chakravarty et al [10] developed OncoKB which is an expert-guided precision oncology knowledge base. Yu et al [11] used deep residual networks to extract two types of relationships in Wikipedia and constructed a food domain knowledge graph. Most of the knowledge in these existing domain knowledge graphs is obtained from existing databases or structured and semi-structured knowledge in online encyclopaedias, which ignored a large number of unstructured texts describing unclassified relationships between concepts and instances.…”

Section: Introductionmentioning

confidence: 99%

A domain knowledge graph construction method based on Wikipedia

Mao

et al. 2020

Journal of Information Science

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In order to achieve real-time updating of the domain knowledge graph and improve the relationship extraction ability in the construction process, a domain knowledge graph construction method is proposed. Based on the structured knowledge in Wikipedia’s classification system, we acquire concepts and instances contained in subject areas. A relationship extraction algorithm based on co-word analysis is intended to extract the classification relationships in semi-structured open labels. A Bi-GRU remote supervised relationship extraction model based on a multiple-scale attention mechanism and an improved cross-entropy loss function is proposed to obtain the non-classification relationships of concepts in unstructured texts. Experiments show that the proposed model performs better than the existing methods. Based on the obtained concepts, instances and relationships, a domain knowledge graph is constructed and the domain-independent nodes and relationships contained in them are removed through a vector variance algorithm. The effectiveness of the proposed method is verified by constructing a food domain knowledge graph based on Wikipedia.

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A relationship extraction method for domain knowledge graph construction

Cited by 56 publications

References 22 publications

Information Extraction for Intestinal Cancer Electronic Medical Records

Information Extraction for Intestinal Cancer Electronic Medical Records

Distant Supervision for Relations Extraction via Deep Residual Learning and Multi-instance Attention in Cybersecurity

A domain knowledge graph construction method based on Wikipedia

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