Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning

Challenge:	Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks.
Approach:	They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration.
Outcome:	The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks.

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Overview

Challenge

Approach

Outcome

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Jiho Kim, Yeonsu Kwon, Yohan Jo, Edward Choi

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Challenge:	Using large language models for complex reasoning tasks on knowledge graphs remains unexplored.
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Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

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Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang

Semantic Text Processing

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
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Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)

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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Semantic Text Processing

Challenge:	Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs).
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Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion (2022.coling-1)

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Chen Chen, Yufei Wang, Bing Li, Kwok-Yan Lam

Semantic Text Processing

Challenge:	Knowledge Graph Completion (KGC) has been extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC.
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Outcome:	The proposed framework outperforms many competitive baselines and sets new state-of-the-art performance on five benchmarks.

Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning (2025.emnlp-main)

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Yu Liu, Yanan Cao, Xixun Lin, Yanmin Shang, Shi Wang, Shirui Pan

Semantic Text Processing

Challenge:	Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes.
Approach:	They propose a framework that enhances LLMs for KGC via structure-aware alignment-tuning to align graph embeddings with the natural language space through multi-task contrastive learning.
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Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)

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Guangqian Yang, Yi Liu, Lei Zhang, Licheng Zhang, Hongtao Xie, Zhendong Mao

Semantic Text Processing

Challenge:	Text-based knowledge graph completion methods neglect knowledge contexts in inferring process.
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KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion (2023.findings-emnlp)

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Yanbin Wei, Qiushi Huang, Yu Zhang, James Kwok

Semantic Text Processing

Challenge:	Existing knowledge graph completion methods struggle with long-tail entities due to limited structural information and imbalanced distributions of entities.
Approach:	They propose a framework that integrates a large language model and a triple-based KGC retriever to alleviate the long-tail problem without incurring additional training overhead.
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Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)

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Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin

Structured Data in NLP

Challenge:	Existing knowledge graphs are far from complete with large portions of triplets missing.
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DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)

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Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang

Semantic Text Processing

Challenge:	Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential .
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