knowledge-graphs

The author argues that GraphRAG is fundamentally a data modeling problem rather than just a retrieval algorithm, proposing a five-component architecture using ontologies, knowledge graphs, and an MCP server for unified agent memory.

This paper introduces a method using knowledge-graph paths as intermediate supervision to improve self-evolving search agents. It addresses bottlenecks in Search Self-Play by grounding question construction in relational context and introducing a Waypoint Coverage Reward for graded partial credit.

This paper introduces TGS-RAG, a bidirectional verification and completion framework that synergizes text-based and graph-based Retrieval-Augmented Generation to improve multi-hop reasoning accuracy.

This paper introduces SPARK, a self-play reinforcement learning framework that leverages knowledge graphs derived from scientific literature to improve relational reasoning in vision-language models.

This paper introduces a unified benchmark to evaluate the robustness of Graph Neural Networks on noisy, text-derived knowledge graphs and the effectiveness of graph construction methods in the biomedical domain.

This paper introduces NATD-GSSL, a framework evaluating the robustness of Graph Self-Supervised Learning on noisy, text-driven biomedical graphs. It demonstrates that certain GNN architectures and pretext tasks maintain performance despite real-world noise, offering practical guidance for unsupervised learning in imperfect datasets.

CocoIndex is a tool that provides continuously fresh context for AI agents by incrementally updating embeddings, summaries, and knowledge graphs from codebases, docs, PDFs, Slack, meeting notes, and videos.

LogosKG introduces a hardware-aligned framework for scalable, interpretable multi-hop retrieval on billion-edge knowledge graphs, integrating degree-aware partitioning and on-demand caching to boost efficiency without sacrificing fidelity.

Researchers from Arizona State University present a framework for evaluating adaptive personalization of educational reading materials using theory-grounded simulated learners, incorporating memory models, misconception revision, and Bayesian Knowledge Tracing. Experiments across three subjects show adaptive reading significantly improved outcomes in computer science but had mixed results in chemistry and biology.

This paper compares two strategies for injecting structured biomedical knowledge from the UMLS Metathesaurus into language models: continual pretraining (embedding knowledge into model parameters) and GraphRAG (querying a knowledge graph at inference time). Results show improvements on biomedical QA benchmarks, with GraphRAG on LLaMA 3-8B yielding over 3 and 5 accuracy points on PubMedQA and BioASQ respectively without any retraining.

This paper proposes M-Hyper, a novel multi-modal knowledge graph completion method that balances fusion and independence of modality representations using hypercomplex (biquaternion) algebra. The approach introduces Fine-grained Entity Representation Factorization and Robust Relation-aware Modality Fusion modules to achieve state-of-the-art performance with improved robustness.