Papers with knowledge fusion
Knowledge Graph Alignment with Entity-Pair Embedding (2020.emnlp-main)
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| Challenge: | Existing methods for Knowledge Graph (KG) alignment are not satisfactory. |
| Approach: | They propose a method that directly learns embeddings of entity-pairs for KG alignment. |
| Outcome: | The proposed approach can achieve state-of-the-art on five real-world datasets. |
A Localized Geometric Method to Match Knowledge in Low-dimensional Hyperbolic Space (2022.emnlp-main)
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| Challenge: | Existing methods for entity alignment are limited to Euclidean space and hyperbolic embedding can represent hierarchical structure in knowledge graphs. |
| Approach: | They propose a localized geometric method to find equivalent entities in hyperbolic space using a hyperbolical neural network. |
| Outcome: | The proposed method outperforms the state-of-the-art by a large margin. |
CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)
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| Challenge: | Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese . |
| Approach: | They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure . |
| Outcome: | The proposed corpus is based on 100 documents from diverse domains and is publicly accessible. |
To See a World in a Spark of Neuron: Disentangling Multi-Task Interference for Training-Free Model Merging (2025.emnlp-main)
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Zitao Fang, Guodong Du, Shuyang Yu, Yifei Guo, Yiwei Zhang, Yiyao Cao, Jing Li, Ho-Kin Tang, Sim Kuan Goh
| Challenge: | Existing approaches to model merging ignore the fundamental roles of neurons, connectivity and activation. |
| Approach: | They propose a framework that relies on neuronal mechanisms to mitigate task interference . they decomposed task-specific representations into two complementary subspaces . their results offer new insights into mitigating task interference and improving knowledge fusion . |
| Outcome: | The proposed framework reduces task interference within neurons and improves knowledge fusion. |
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)
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Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian
| Challenge: | Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics. |
| Approach: | They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities. |
| Outcome: | The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly. |
EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)
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| Challenge: | Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object. |
| Outcome: | The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets. |
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)
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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Foundational models and their checkpoints have advanced deep learning, boosting performance across applications. |
| Approach: | They propose a method for pruning fine-tuned models by calculating differences between them and original model. |
| Outcome: | The proposed method can improve performance across vision, NLP, and multi-modal benchmarks. |