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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Enhancing cross-evidence reasoning graph for document-level relation extraction.

Qiankun Pi1,2, Jicang Lu1,2, Taojie Zhu1,2

  • 1PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China.

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|July 10, 2024
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Summary
This summary is machine-generated.

This study introduces a new document-level relation extraction (RE) model using an Enhancing Cross-evidence Reasoning Graph (ECRG). The model improves performance by pre-extracting key evidence sentences and constructing graphs to capture entity relationships.

Keywords:
Document-level REEntity-level graphEvidence graph

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Extraction

Background:

  • Document-level relation extraction (RE) aims to identify semantic connections between entities in a document.
  • Existing methods often struggle with noisy information from irrelevant sentences.
  • Entities are frequently distributed across multiple sentences, necessitating cross-sentence relation prediction.

Purpose of the Study:

  • To develop a novel document-level RE model that enhances performance by effectively utilizing evidence sentences.
  • To address the challenge of noisy information in document-level RE by pre-selecting relevant evidence.
  • To improve the mining of semantic information and distant interactions between entities within documents.

Main Methods:

  • A document-level RE model leveraging an Enhancing Cross-evidence Reasoning Graph (ECRG) was developed.
  • An evidence extraction rule based on the center-sentence was designed to pre-extract high-quality evidence.
  • Evidence graphs were constructed to mine intra-evidence mention connections, and entity-level graphs aggregated mentions for inter-entity distant interactions.

Main Results:

  • The proposed ECRG model demonstrated improved performance on document-level relation extraction tasks.
  • Pre-extraction of evidence sentences using the center-sentence rule effectively reduced noisy information.
  • The graph-based approach successfully captured both local and distant entity interactions.

Conclusions:

  • The ECRG model offers a more effective approach to document-level relation extraction.
  • Leveraging evidence graphs and entity-level graphs enhances the ability to mine complex semantic relationships.
  • The findings suggest that strategic evidence selection and graph construction are crucial for robust RE.