# Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective arXiv: https://arxiv.org/abs/2412.10390 ยท HTML: https://mlsanity.com/paper/2412.10390/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. - Builds on 14 of its 91 references that are in the map. - First posted on arXiv 30 Nov 2024. ## Placed in - LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/) ## Builds on (most cited first, 14 of 14) - [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024) - [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014) - [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019) - [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023) - [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021) - [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017) - [Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2307.07697/) (2023) - [Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings](https://mlsanity.com/paper/2002.05969/) (2020) - [GreaseLM: Graph REASoning Enhanced Language Models for Question Answering](https://mlsanity.com/paper/2201.08860/) (2022) - [RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2010.04029/) (2020) - [Complex Query Answering with Neural Link Predictors](https://mlsanity.com/paper/2011.03459/) (2020) - [KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models](https://mlsanity.com/paper/2310.11220/) (2023) - [TensorLog: A Differentiable Deductive Database](https://mlsanity.com/paper/1605.06523/) (2016) - [Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs](https://mlsanity.com/paper/2310.11246/) (2023) ## Built on by (most cited first, 20 of 20) - [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025) - [Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding](https://mlsanity.com/paper/2505.19825/) (2025) - [MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://mlsanity.com/paper/2509.21391/) (2025) - [FinRule-Bench: A Benchmark for Joint Reasoning over Financial Tables and Principles](https://mlsanity.com/paper/2603.11339/) (2026) - [LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs](https://mlsanity.com/paper/2512.22266/) (2025) - [Multi-hop Reasoning and Retrieval in Embedding Space: Leveraging Large Language Models with Knowledge](https://mlsanity.com/paper/2603.13266/) (2026) - [Neural-Symbolic Logic Query Answering in Non-Euclidean Space](https://mlsanity.com/paper/2603.15633/) (2026) - [Prompt-tuning with Attribute Guidance for Low-resource Entity Matching](https://mlsanity.com/paper/2603.19321/) (2026) - [Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering](https://mlsanity.com/paper/2505.08155/) (2025) - [On the Generalization Gap in LLM Planning: Tests and Verifier-Reward RL](https://mlsanity.com/paper/2601.14456/) (2026) - [Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2602.12389/) (2026) - [Mixture of Demonstrations for Textual Graph Understanding and Question Answering](https://mlsanity.com/paper/2603.23554/) (2026) - [An Automated Theorem Generator with Theoretical Foundation Based on Rectangular Standard Contradiction](https://mlsanity.com/paper/2511.04092/) (2025) - [Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge](https://mlsanity.com/paper/2601.10485/) (2026) - [Diagnosing Structural Failures in LLM-Based Evidence Extraction for Meta-Analysis](https://mlsanity.com/paper/2602.10881/) (2026) - [Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables](https://mlsanity.com/paper/2605.25985/) (2026) - [Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks](https://mlsanity.com/paper/2606.07583/) (2026) - [Neurosymbolics for Data Engineering: Achieving Long Context Token Reduction Without Finetuning](https://mlsanity.com/paper/2609.00367/) (2026) - [NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing](https://mlsanity.com/paper/2609.00604/) (2026) - [Robust Conformal Consensus: Multi-Agent LLM-as-a-Judge Interval Evaluation with Conformal Prediction](https://mlsanity.com/paper/2609.06367/) (2026) ## Abstract (arXiv) Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new information. Traditional symbolic reasoning, despite its strengths, struggles with the challenges posed by incomplete and noisy data within these graphs. In contrast, the rise of Neural Symbolic AI marks a significant advancement, merging the robustness of deep learning with the precision of symbolic reasoning. This integration aims to develop AI systems that are not only highly interpretable and explainable but also versatile, effectively bridging the gap between symbolic and neural methodologies. Additionally, the advent of large language models (LLMs) has opened new frontiers in knowledge graph reasoning, enabling the extraction and synthesis of knowledge in unprecedented ways. This survey offers a thorough review of knowledge graph reasoning, focusing on various query types and the classification of neural symbolic reasoning. Furthermore, it explores the innovative integration of knowledge graph reasoning with large language models, highlighting the potential for groundbreaking advancements. This comprehensive overview is designed to support researchers and practitioners across multiple fields, including data mining, AI, the Web, and social sciences, by providing a detailed understanding of the current landscape and future directions in knowledge graph reasoning.