The paper introduces Logic-Guided Graph Denoising Diffusion Models, which integrate First Order Logic (FOL) constraints into graph diffusion models via classifier guidance. By conditioning the denoising trajectory on a fuzzy, differentiable relaxation of logical rules, the method steers generation toward graphs that satisfy domain-specific structural constraints. Experiments on knowledge graph generation show a significant increase in the proportion of outputs that comply with ontological rules.
arXiv:2605. 30747v2 Announce Type: replace Abstract: Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns.
The paper introduces PG‑DiGress, a constraint‑guided discrete denoising diffusion model that generates power‑grid feeder topologies while respecting electrical compatibility and radiality rules. By injecting feeder constraints as soft masks during the reverse diffusion process and applying a final projection step, the model learns categorical node and edge patterns from existing feeder data. Evaluation shows a dramatic improvement in rule compliance, raising the strict feeder pass rate from 13.7% to 96.8%, and the generated graphs can be converted into executable feeder models for downstream analysis.
The paper introduces the Structure-Internalized Rule Language Model (SIRLM) to improve Knowledge Graph Reasoning (KGR) by addressing the mismatch between KG structural context and Large Language Model (LLM) parametric knowledge. SIRLM centers on a Structure-Internalized Rule Generator (SIRG) that uses in-context learning, a structural relation memory, a KG tokenizer, and a neuro-symbolic reasoner to generate structural rules and provide faithful rule-execution feedback. Experiments on 36 datasets against 17 state‑of‑the‑art KGR methods show that SIRLM achieves significant performance gains.
arXiv:2603. 14147v2 Announce Type: replace Abstract: The generative artificial intelligence (AI) ecosystem is undergoing rapid transformations that threaten its sustainability.
The paper introduces GLaMoR, a reasoning pipeline that converts OWL ontologies into graph-structured data and applies a Graph Language Model (GLM) for consistency checking. It addresses the challenge of verifying ontology consistency, especially for large ontologies where classical reasoners become computationally expensive. Experiments on NCBO BioPortal ontologies show that GLaMoR achieves 95% accuracy and is 20 times faster than traditional reasoners.
arXiv:2607. 19996v1 Announce Type: new Abstract: Machine Learning models are widely used for automating classification tasks by extracting statistical patterns from data.
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.
The paper investigates how the textual serialization of knowledge graphs influences the performance of large language models (LLMs). It evaluates 15 different graph representation formats across nine advanced LLM configurations on tasks involving ontology-based knowledge extraction and structural graph reasoning. While knowledge extraction shows little sensitivity to serialization, structural reasoning accuracy varies significantly depending on the chosen format, model, query, and task, suggesting that graph serialization should be treated as a dynamic orchestration decision.
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...
arXiv:2607. 22571v1 Announce Type: new Abstract: Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, yet existing agentic approaches that perform well on public benchmarks often fail to generalize to real-world enterprise Knowledge Graphs (KGs), which are dense, schema-driven, and operationally constrained.
arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...
The paper introduces SymbolLKG, a neuro-symbolic framework that combines a Logical Knowledge Graph (LKG) with dynamic solver routing to improve logical reasoning in large language models. The LKG represents logical rules and constraints as topological nodes, enabling explicit modeling of dependencies extracted from text. A Logic Router dispatches tasks to the most suitable symbolic engine, supported by a topology-aware hybrid retrieval mechanism, and the approach outperforms existing prompting and RAG baselines on logical reasoning benchmarks.