repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago
yifanfeng97/Hyper-Extract
Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 51%Set up a retrieval pipeline →
- PossiblePossibly related (embedding) · 48%Measuring Graph-to-Graph Semantic Similarity in Knowledge Graphs: An Empirical Evaluation of Knowledge Graph Embeddings →
- PossiblePossibly related (embedding) · 47%Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs →
- PossiblePossibly related (embedding) · 48%BaryGraph - knowledge graph where every relationship is its own embedded document (not an edge) [R] →
- PossiblePossibly related (embedding) · 60%RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM →
- PossiblePossibly related (embedding) · 46%Show HN: What 180k words look like as a temporal knowledge graph (Oz series) →
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paperEfficient Retrieval-Augmented Generation via Token Co-occurrence GraphspaperRAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLMnewsBaryGraph - knowledge graph where every relationship is its own embedded document (not an edge) [R]tutorialSet up a retrieval pipelinepaperMeasuring Graph-to-Graph Semantic Similarity in Knowledge Graphs: An Empirical Evaluation of Knowledge Graph EmbeddingsnewsShow HN: What 180k words look like as a temporal knowledge graph (Oz series)