https://github.com/DeepGraphLearning/GNN-QE
공식 코드Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Papers with Code 아카이브
Complex Query Answering
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This task is concerned with answering complex queries over incomplete knowledge graphs. In the most simple case, the task is reduced to link prediction: a 1-hop query for predicting the existence of an edge between a pair of nodes. Complex queries are concerned with other structures between nodes, such as 2-hop and 3-paths, and intersecting paths with intermediate variables.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DeepGraphLearning/ULTRA
공식 코드https://github.com/MMMU-Benchmark/MMMU
공식 코드MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
https://github.com/bys0318/qto
공식 코드Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
https://github.com/francoisluus/KGReasoning
공식 코드https://github.com/hkust-knowcomp/ceqa
공식 코드Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
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/lmpnn
공식 코드Logical Message Passing Networks with One-hop Inference on Atomic Formulas
https://github.com/hkust-knowcomp/nrn
공식 코드Knowledge Graph Reasoning over Entities and Numerical Values
https://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/hyren/query2box
공식 코드Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
https://github.com/lhrlab/nqe
공식 코드NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
https://github.com/qianlima-lab/clmpt
공식 코드Conditional Logical Message Passing Transformer for Complex Query Answering
https://github.com/uclnlp/cqd
공식 코드https://github.com/williamleif/graphqembed
공식 코드https://github.com/yaooxu/q2t
공식 코드Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
https://github.com/01-ai/yi
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
https://github.com/Blidge/KGReasoning
https://github.com/Jacfger/simple-stuffs
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
https://github.com/LHY-24/KG-Compilation
https://github.com/MindSpore-scientific-2/code-10/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific-2/code-11/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific-2/code-2/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific-2/code-8/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific/code-13/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/dice-group/dice-embeddings
https://github.com/eric-ai-lab/probmed
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
https://github.com/kjh9503/caqr
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
https://github.com/leloykun/mmfm-challenge
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
https://github.com/opendatalab/pm4bench
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
https://github.com/pminervini/kgreasoning
https://github.com/snap-stanford/KGReasoning
https://github.com/tgebhart/sheaf_kg_transind
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.