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# Knowledge Graph

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  1. Chinese Medicine

    A home triage model for TCM gynecology: MAGR-TCM separates emergencies from routine cases in all 30 safety-test records

    For home health consultation and triage in traditional Chinese medicine gynecology, MAGR-TCM turns 741 clinical case records into a knowledge graph of 10,231 entities and 32,051 relationships, then has four specialized agents reason step by step along the disease-pattern-principle-formula-medicinal chain. On 30 held-out test cases its RAGAS faithfulness is 0.821, against 0.766 for GraphRAG, 0.651 for LightRAG and 0.590 for Naive RAG; on a safety set of 10 emergency and 20 routine cases, the red-flag mechanism intercepted and passed every case, with sensitivity and specificity both 1.000. These results come from retrospective, expert-annotated records and have not yet been validated prospectively with real home users.

    Anhui University of Traditional Chinese Medicine
    Ming Lv（First author）Fang Fang（Corresponding author）
    MAGR-TCMRetrieval-Augmented GenerationKnowledge GraphTraditional Chinese Medicine diagnosis
  2. arXiv

    Knowledge-graph prompts don't need the whole graph: relation knowledge picks a dozen facts, and seven open LLMs answer better

    Knowledge-graph question answering usually ranks facts by relevance and packs them into the prompt, but relevance guarantees neither sufficiency nor consistency. This method uses relation knowledge, either hand-written rules for what relations mean and how they compose or a mapping learned from training questions annotated with relation paths, to search the graph for a compact fact set that derives an answer, then applies a derivability check for whether an answer follows and a consistency check for whether facts conflict, with a validation-chosen gate deciding when the derived answer replaces the LLM's. On WebQSP, seven open LLMs read these contexts, 14.7 facts on average, at 10.2 exact-match points above SubgraphRAG's 100 facts.

    Seunghui Jwa（First author）
    Compact Sufficient Context SelectionDerivability CheckConsistency CheckKnowledge GraphRouting Gate
  3. NVIDIA/auto-ontology

    NVIDIA open-sources Auto Ontology: an enterprise context layer that aligns metric definitions before an agent answers

    NVIDIA released Auto Ontology, an open library that builds a context layer from enterprise metadata and tells AI agents where data lives, what a metric means, whether to use it, how to use it, where it came from, and what is similar. It generates enterprise ontologies from metadata, connects existing ontologies and semantic models, and reconciles conflicting definitions across systems. It then exposes that context to any agent harness through MCP, CLI, and API, returning provenance with every answer. The library is distributed under Apache License 2.0 and is not currently accepting external contributions.

    NVIDIA
    Auto OntologyText-to-SQLKnowledge Graph
  4. bioRxiv

    Biomedical knowledge-graph QA stops relying on document retrieval alone: three systems run in parallel and merge, lifting satisfaction from 36% to 86%

    After GeneNetwork's 1TB structured database was remodeled as an RDF graph alongside a literature corpus, a system that runs document, graph and agentic retrieval-augmented generation in parallel and merges their answers raised the satisfaction rate from 36% for plain document retrieval to 86% on 40 expert questions, while delegating most work to a local model still reached 59%.

    University of Tennessee Health Science CenterPwani UniversityUniversity College London
    Johannes Fenoude Pancrace Medagbe（First author）Pjotr Prins（Corresponding author）
    Expert-Curated BenchmarkRetrieval-Augmented GenerationSatisfaction Frequency ψKnowledge GraphAgentic Retrieval-Augmented Generation
  5. arXiv

    SciMuse generates personalized research ideas from a 58-million-paper knowledge graph and an LLM; over 100 research group leaders rated 4,400+ ideas at a mean of 2.40 on a 5-point scale, with 24.9% rated 4 or 5

    The authors introduce SciMuse, which generates personalized research ideas using a knowledge graph of 58 million papers and a large language model, and they conducted a large-scale evaluation in which more than 100 research group leaders spanning the natural sciences to the humanities rated over 4,400 personalized ideas by level of interest; expert ratings were modest (mean 2.40 on a 5-point scale, most common rating 1) while 24.9% of ideas were rated 4 or 5, supplying knowledge-graph-selected concept pairs did not improve expert-rated interest over a titles-only GPT baseline, high-citation-predicted pairs even showed a weak tendency (1.

    Xuemei Gu（First author）
    SciMuseAI scientific idea generationKnowledge GraphLarge Language Model

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