Knowledge Graph Completion
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Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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공식 코드https://github.com/teddy-xionggz/mirage
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https://github.com/tengxiao1/gsil
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https://github.com/terry-cyx/metaopenfoam
공식 코드https://github.com/teshnizi/optimus
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https://github.com/tetis-nlp/landmatrix-graphql-python
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https://github.com/tgebhart/sheaf_kg_transind
공식 코드Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference
https://github.com/the-bird-F/GLM-Voice-RAG
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https://github.com/theryanl/egs
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https://github.com/theworldofagents/agentic-reasoning
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https://github.com/thijmennijdam/NevIR-reproducibility
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https://github.com/thisishwang/exit
공식 코드EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.