Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation Extraction

Al-Sabri, Raeed; Gao, Jianliang; Chen, Jiamin; Oloulade, Babatounde Moctard; Lyu, Tengfei

doi:10.1109/tcbb.2022.3205113

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…For instance, Srivastava et al [21] proposed a self-attention-based relationship extraction model that is capable of capturing entity relationships with directionality. Al-Sabri et al [22] designed a multi-view graph neural network automated modeling framework for biomedical entity and relationship extraction, which can solve the multi-view problem present in entity relationships. Zhang et al [23] proposed an RNN-based relationship extraction model to learn remote dependencies between entity relationships.…”

Section: Knowledge Extractionmentioning

confidence: 99%

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

Qu,

Zhang,

Liang

et al. 2023

Electronics

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The grid terminal deploys numerous types of communication equipment for the digital construction of the smart grid. Once communication equipment failure occurs, it might jeopardize the safety of the power grid. The massive amount of communication equipment leads to a dramatic increase in fault research and judgment data, making it difficult to locate fault information in equipment maintenance. Therefore, this paper designs a knowledge-graph-driven method for intelligent decision making on power communication equipment faults. The method consists of two parts: power knowledge extraction and user intent multi-feature learning recommendation. The power knowledge extraction model utilizes a multi-layer bidirectional encoder to capture the global features of the sentence and then characterizes the deep local semantics of the sentence through a convolutional pooling layer, which achieves the joint extraction and visual display of the fault entity relations. The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. The experimental results show that the method is optimal in knowledge extraction compared to classical models such as BERT-CRF, in which the F1 value reaches 81.7%, which can effectively extract fault knowledge. User intent multi-feature learning recommendation works best, with an F1 value of 87%. Compared with the classical models such as CKAN and KGCN, it is improved by 5%~11%, which can effectively solve the problem of insufficient mining of user retrieval intent. This method realizes accurate retrieval and personalized recommendation of fault information of electric power communication equipment.

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Section: Knowledge Extractionmentioning

confidence: 99%

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

Qu,

Zhang,

Liang

et al. 2023

Electronics

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“…This task has been labeled "Rel+" in some papers, in contrast to the entity-relationship joint extraction task, which does not consider unrelated entities when extracting quintuples. This can be seen in the recent datasets, such as scientific papers [1], the financial domain [2], biomedical information [3], and energy data [4].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific Literature

Liu,

Fu,

Xia

et al. 2024

Applied Sciences

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Extracting relation quintuple and feature words from unstructured text is a prelude to the construction of the scientific knowledge base. At present, the prior works use explicit clues between entities to study this task but ignore the use and the association of the feature words. In this work, we propose a new method to generate self-adaptive feature words from the original text for every single sample. These words can add additional correlation information to the knowledge graph. We allow the model to generate a new word representation and apply it to the original sentence to judge the relation type and locate the head and tail of the relation quintuple. Compared with the previous works, the feature words increase the flexibility of relying on information and improve the explanatory ability. Extensive experiments on scientific field datasets illustrate that the self-adaptive feature words method (SAFW) is good at ferreting out the unique feature words and obtaining the core part for the quintuple. It achieves good performance on four public datasets and obtains a markable performance improvement compared with other baselines.

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“…With the rapid accumulation of biological network data, GNN has become an effective tool in bioinformatics tasks ( Zhang et al, 2021 ). Taking drug development as an example, it has been proven a practical way of achieving greater efficiency in drug attribute prediction ( Gu et al, 2021 ), drug side effect prediction ( Zitnik et al, 2018 ), relationship extraction ( Al-Sabri et al, 2022 ), etc.…”

Section: Introductionmentioning

confidence: 99%

Drug–disease association prediction with literature based multi-feature fusion

Kang

Hou

et al. 2023

Front. Pharmacol.

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Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge.Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism.Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916.Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

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Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation Extraction

Cited by 12 publications

References 36 publications

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific Literature

Drug–disease association prediction with literature based multi-feature fusion

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