Type affinity network for distantly supervised relation extraction

Distantly Supervised Relation Extraction (DSRE) focuses on mining relational information between entities from text, which is a critical step in constructing and expanding knowledge graphs. However, this task typically faces the problem of long-tail distribution of relationships. Furthermore, much previous research has been limited to specific relational features, overlooking the potential discriminative information in the dependencies among these features. To address these issues, we propose a Type Affinity Network (TAN) that explicitly models the dependencies among relational features. Using differentiated entity-type information and local context information, we extract high-quality feature representations. The dynamic integration of type dependency information via a type affinity matrix effectively leverages potential discriminative information, enhancing relation extraction accuracy. Furthermore, aggregating implicit features of relationships forms base points as references, improving the ability of the model to handle long-tail relations. These relationship base points differ from traditional hierarchical structures, providing a more general method. We conduct extensive experiments on two benchmark DSRE datasets, namely NYT-10 and WIKI-20 m. Compared with multiple state-of-the-art DSRE methods, the TAN not only shows significant performance improvements in handling long-tail relations, but also demonstrates overall performance improvement. • Proposed the Type Affinity Network (TAN): Designed TAN model integrating entity types and context. • Designed the Dynamic Type Affinity Matrix (TAM): Developed TAM to capture type dependencies, boosting accuracy. • Utilized Advanced Relationship Feature Modeling: Used base points to aggregate implicit features for long-tail relations. • Comprehensive Performance Validation: Demonstrated TAN outperforms DSRE methods on NYT-10 and WIKI-20m. • Enhanced Feature Representations: Designed mechanisms to model entity types and implicit dependencies.

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Publication Details

Journal
Neurocomputing
Published
2025-02-18
DOI
https://doi.org/10.1016/j.neucom.2025.129684
Citations
4
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
9.92

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article

Type affinity network for distantly supervised relation extraction

Juncheng Zhou, Xiao Liu, Wei Song
4 citations
Neurocomputing
Topic Modeling
9.92
article

Type affinity network for distantly supervised relation extraction

Juncheng Zhou, Xiao Liu, Wei Song
article en
4 citations

Abstract

Distantly Supervised Relation Extraction (DSRE) focuses on mining relational information between entities from text, which is a critical step in constructing and expanding knowledge graphs. However, this task typically faces the problem of long-tail distribution of relationships. Furthermore, much previous research has been limited to specific relational features, overlooking the potential discriminative information in the dependencies among these features. To address these issues, we propose a Type Affinity Network (TAN) that explicitly models the dependencies among relational features. Using differentiated entity-type information and local context information, we extract high-quality feature representations. The dynamic integration of type dependency information via a type affinity matrix effectively leverages potential discriminative information, enhancing relation extraction accuracy. Furthermore, aggregating implicit features of relationships forms base points as references, improving the ability of the model to handle long-tail relations. These relationship base points differ from traditional hierarchical structures, providing a more general method. We conduct extensive experiments on two benchmark DSRE datasets, namely NYT-10 and WIKI-20 m. Compared with multiple state-of-the-art DSRE methods, the TAN not only shows significant performance improvements in handling long-tail relations, but also demonstrates overall performance improvement. • Proposed the Type Affinity Network (TAN): Designed TAN model integrating entity types and context. • Designed the Dynamic Type Affinity Matrix (TAM): Developed TAM to capture type dependencies, boosting accuracy. • Utilized Advanced Relationship Feature Modeling: Used base points to aggregate implicit features for long-tail relations. • Comprehensive Performance Validation: Demonstrated TAN outperforms DSRE methods on NYT-10 and WIKI-20m. • Enhanced Feature Representations: Designed mechanisms to model entity types and implicit dependencies.

NeurocomputingVol. 630
Jiangnan University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 3%
Topic Modeling
9.92
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