Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks

Jiang, Bo; Cao, Jia

doi:10.3390/app13020842

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…These methods specifically address the issue of entity relation overlap. NovelTagging 10 and TPlinker BERT 35 employ improved sequence labeling techniques to solve the triplet overlap problem. CopyRE 31 and GraphRel 30 are end-to-end information extraction models.…”

Section: Methodsmentioning

confidence: 99%

“…For example, Chen et al 9 proposed a novel feature engineering method to improve entity-relationship extraction performance. Jiang and Cao 10 introduced an unknown heterogeneous graph attention network to enhance semantic analysis and fusion between entities and relationships, thereby improving entity–relationship extraction. However, when these models encounter complex relationships, although they can partially address the problem of triple element overlap, they still struggle to achieve accurate extraction and matching when multiple overlapping triplets exist within a single sentence.…”

Section: Introductionmentioning

confidence: 99%

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CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management

Du,

Wang,

Zhu

et al. 2024

Science Progress

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In recent years, hazardous chemical incidents have occurred frequently, resulting in significant human casualties, property damage, and environmental pollution due to human or natural factors. Accurately mining the lessons learned from accumulating incident reports and constructing the knowledge graph for hazardous chemical incident management can assist managers in identifying patterns and analyzing common attributes, thereby preventing the recurrence of similar incidents. This article addresses the challenges of dispersed textual information, specialized vocabulary, and data formats in hazardous chemical incidents. We propose a novel entity-relation extraction model called CPBA-CLIM (content-position-based attention-cross-label intersect matching) to provide an accurate data foundation for constructing the hazardous chemical incident knowledge graph. The content-position-based attention module, based on content-position attention, incorporates contextual semantic information into the combined encoding of bidirectional encoder representations from the transformer's content and position to obtain dynamic word vectors that align with the thematic context of the text. Additionally, the cross-label intersect matching strategy evaluates the rationality of entity–relation interactions in sets containing potential overlaps, reducing the impact of entity–relation overlap on triplet extraction accuracy. Comparative experimental results on public datasets demonstrate the model's outstanding performance in overlapping triplets. Qualitative experiments on a self-constructed dataset integrate our model with ontology construction techniques, successfully establishing a knowledge graph for managing hazardous chemical incidents. This research effectively enhances the degree of automation and efficiency in knowledge graph construction, thus offering support and decision-making foundations for hazardous chemical safety management.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management

Du,

Wang,

Zhu

et al. 2024

Science Progress

View full text Add to dashboard Cite

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Subject Knowledge Entity Relationship Extraction Based on Multi-feature Fusion and Relation Specific Horns Tagging

Tian,

Pei,

2024

Knowledge Science, Engineering and Management

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A Parallel Model for Jointly Extracting Entities and Relations

Chen,

Zheng,

et al. 2024

Neural Process Lett

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Extracting relational triples from a piece of text is an essential task in knowledge graph construction. However, most existing methods either identify entities before predicting their relations, or detect relations before recognizing associated entities. This order may lead to error accumulation because once there is an error in the initial step, it will accumulate to subsequent steps. To solve this problem, we propose a parallel model for jointly extracting entities and relations, called PRE-Span, which consists of two mutually independent submodules. Specifically, candidate entities and relations are first generated by enumerating token sequences in sentences. Then, two independent submodules (Entity Extraction Module and Relation Detection Module) are designed to predict entities and relations. Finally, the predicted results of the two submodules are analyzed to select entities and relations, which are jointly decoded to obtain relational triples. The advantage of this method is that all triples can be extracted in just one step. Extensive experiments on the WebNLG*, NYT*, NYT and WebNLG datasets show that our model outperforms other baselines at 94.4%, 88.3%, 86.5% and 83.0%, respectively.

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Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks

Cited by 4 publications

References 36 publications

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management

Subject Knowledge Entity Relationship Extraction Based on Multi-feature Fusion and Relation Specific Horns Tagging

A Parallel Model for Jointly Extracting Entities and Relations

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