Papers with Code 아카이브
Knowledge Graphs
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A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hkuds/autoagent
공식 코드AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
https://github.com/hkuds/diffkg
공식 코드https://github.com/hkuds/lightrag
공식 코드https://github.com/hkuds/minirag
공식 코드MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
https://github.com/hkuds/opengraph
공식 코드https://github.com/hkuds/videorag
공식 코드VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
https://github.com/hkunlp/batch-prompting
공식 코드Batch Prompting: Efficient Inference with Large Language Model APIs
https://github.com/hkust-knowcomp/abductivekgr
공식 코드Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation
https://github.com/hkust-knowcomp/atomic-conceptualization
공식 코드Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization
https://github.com/hkust-knowcomp/autoschemakg
공식 코드https://github.com/hkust-knowcomp/ceqa
공식 코드Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
https://github.com/hkust-knowcomp/co2lm
공식 코드CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse Relations
https://github.com/hkust-knowcomp/complexhyperbolickge
공식 코드Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
https://github.com/hkust-knowcomp/efo-1-qa-benchmark
공식 코드Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
https://github.com/hkust-knowcomp/efok-cqa
공식 코드$\text{EFO}_{k}$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
https://github.com/hkust-knowcomp/eventground
공식 코드EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs
https://github.com/hkust-knowcomp/fit
공식 코드Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors
https://github.com/hkust-knowcomp/gold
공식 코드Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection
https://github.com/hkust-knowcomp/lmpnn
공식 코드Logical Message Passing Networks with One-hop Inference on Atomic Formulas
https://github.com/hkust-knowcomp/mico
공식 코드MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation
https://github.com/hkust-knowcomp/nrn
공식 코드Knowledge Graph Reasoning over Entities and Numerical Values
https://github.com/hkust-knowcomp/pipenet
공식 코드PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs
https://github.com/hkust-knowcomp/privatengdb
공식 코드https://github.com/hkust-knowcomp/query2particles
공식 코드Query2Particles: Knowledge Graph Reasoning with Particle Embeddings
https://github.com/hkust-knowcomp/sessioncqa
공식 코드Understanding Inter-Session Intentions via Complex Logical Reasoning
https://github.com/hkust-knowcomp/sqe
공식 코드Sequential Query Encoding For Complex Query Answering on Knowledge Graphs
https://github.com/hkust-knowcomp/vd-pcr
공식 코드VD-PCR: Improving Visual Dialog with Pronoun Coreference Resolution
https://github.com/hkust-knowcomp/wfre
공식 코드https://github.com/hkust-nlp/pem_composition
공식 코드Composing Parameter-Efficient Modules with Arithmetic Operations
https://github.com/hkust-zhiyao/rtllm
공식 코드RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model
https://github.com/hliulab/tcrlm
공식 코드https://github.com/hmchuong/CoLLM
공식 코드https://github.com/hmthanh/GCAT
공식 코드Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
https://github.com/hmuus01/educational_qg
공식 코드Scalable Educational Question Generation with Pre-trained Language Models
https://github.com/hmxiong/3ur-llm
공식 코드3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene Understanding
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