arXiv AI

Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective

The article surveys neural-symbolic reasoning over knowledge graphs from a query perspective, highlighting the limitations of traditional symbolic methods when dealing with incomplete or noisy data. It discusses how the fusion of deep learning and symbolic reasoning—termed Neural Symbolic AI—offers interpretable and versatile solutions, and examines the role of large language models in advancing knowledge graph inference. The survey provides a comprehensive review of query types, classification of neural-symbolic approaches, and future directions for integrating LLMs with knowledge graph reasoning.

Hugging Face Trending Papers

Jun 10

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpins critical applications across social, biological, financial, transportation, web, and knowledge domains, making it essential to understand how LLMs can leverage graph computation for grounded, context-rich inference.

llms agents

arXiv AI

Jun 11

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

arXiv:2606. 11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems.

By Arijit Khan, Longxu Sun, Xin Huang

llms agents

arXiv Machine Learning

Jun 5

Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion

arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.

By Dongxiao He, Ruqiong Zhang, Zhizhi Yu, Ling Ding, Di Jin, Guangquan Xu, Zhiyong Feng

llms benchmarks

arXiv AI

Jun 19

Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

arXiv:2606. 19351v1 Announce Type: cross Abstract: Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support.

By Xinyan Zhu, Yaoqi Liu, Yue Gao, Huadong Ma, Cheng Yang, Chuan Shi

llms nlp benchmarks safety

arXiv AI

Sep 10

Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction via Ontology-grounded Post-extraction Correction

The paper introduces a neuro‑symbolic framework for constructing knowledge graphs (KGs) that are grounded in an ontology. It combines open‑domain extraction, embedding‑based canonicalization of types and predicates, and a post‑extraction LLM‑based correction step to fix ontology violations, thereby reducing token usage and improving KG consistency. The resulting KGs support symbolic querying, as evidenced by the prevalence of SPARQL graph patterns in the extracted data.

By Lorenzo Loconte, Timothy Hospedales, Cristina Cornelio

llms rag nlp data-engineering

arXiv AI

Jul 14

Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey

arXiv:2607. 09666v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data.

By Chengcheng Sun, Jiayun Tian, Cheng Zhai, Zhixiao Wang, Yajie Song, Xiaobin Rui, Jian Zhang, Philip S. Yu

rag

arXiv AI

Jul 23

NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering

arXiv:2602. 15353v3 Announce Type: replace-cross Abstract: Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference.

By Rong Fu, Yang Li, Zeyu Zhang, Jiekai Wu, Yaohua Liu, Shuaishuai Cao, Yangchen Zeng, Yuhang Zhang, Xiaojing Du, Simon Fong

llms rag nlp benchmarks

arXiv Computation and Language

Oct 19

Learning to Retrieve and Reason on Knowledge Graph through Active Self-Reflection

arXiv:2502.14932v2 Announce Type: replace Abstract: Extensive research has investigated the integration of large language models (LLMs) with knowledge graphs to enhance the reasoning process. However...

By Han Zhang, Langshi Zhou, Hanfang Yang

llms safety

arXiv Machine Learning

Sep 10

Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

arXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are...

By Xiaohua Lu, Liubov Tupikina, Mehwish Alam

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arXiv AI

Sep 2

KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering

KGFR introduces a Knowledge Graph Foundation Retriever that collaborates with large language models to enhance knowledge‑intensive question answering. By encoding relations with LLM‑generated descriptions and initializing entities from question roles, KGFR enables zero‑shot generalization to unseen knowledge graphs. Its Asymmetric Progressive Propagation technique efficiently handles large graphs, while a controllable reasoning loop allows the LLM to request candidate answers, supporting facts, and reasoning paths.

By Yuanning Cui, Zequn Sun, Wei Hu, Zhangjie Fu

llms nlp

arXiv AI

Aug 6

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

arXiv:2604. 12503v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios.

By Shuai Wang, Xixi Wang, Yinan Yu

llms rag nlp benchmarks safety

arXiv Machine Learning

Oct 22

RelAgent: LLM Agents as Data Scientists for Relational Learning

RelAgent is an LLM-based autonomous data scientist designed for relational learning. It operates in two phases: first, the LLM agent constructs SQL feature programs and selects a predictive model using database, validation, and evaluation tools; second, the resulting program is executed without further LLM calls. The final predictor combines SQL queries and a classical model, providing fast, deterministic, and interpretable predictions that rely solely on human-readable query-defined features.

By Xingyue Huang, Louis Tichelman, Jinwoo Kim, Krzysztof Olejniczak, \.Ismail \.Ilkan Ceylan

llms agents data-engineering

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