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Knowledge Base Question Answering
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Knowledge Base Q&A is the task of answering questions from a knowledge base.
<span style="color:grey; opacity: 0.6">( Image credit: Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rajarshd/cbr-subg
공식 코드Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
https://github.com/ruckbreasoning/subgraphretrievalkbqa
공식 코드Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
https://github.com/salesforce/rng-kbqa
공식 코드RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering
https://github.com/siat-nlp/TransDG
공식 코드Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
https://github.com/smart-task/smart-dataset
공식 코드SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
https://github.com/stanford-oval/spinach
공식 코드SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
https://github.com/stanford-oval/wikidata-emnlp23
공식 코드https://github.com/tan92hl/complex-question-answering-evaluation-of-chatgpt
공식 코드https://github.com/tan92hl/complex-question-answering-evaluation-of-gpt-family
공식 코드https://github.com/tttttttty/alcqa
공식 코드https://github.com/vladislavneon/RuBQ
공식 코드RuBQ: A Russian Dataset for Question Answering over Wikidata
https://github.com/wudapeng268/KBQA-Adapter
공식 코드Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
https://github.com/yanmenxue/rah-kbqa
공식 코드Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
https://github.com/yhshu/distribution-shifts-for-kbqa
공식 코드Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
https://github.com/zzaebok/sparkle
공식 코드SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decoding
https://github.com/IBM/transition-amr-parser
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
https://github.com/INK-USC/MHGRN
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
https://github.com/LiberAI/NSpM
https://github.com/SteveTANTAN/PoG
Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
https://github.com/ag-sc/AMUSE
AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data
https://github.com/dengyang17/dengyang17.github.io
https://github.com/hugochan/PFoodReq
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
https://github.com/microsoft/KC/tree/main/papers/ReTraCk
https://github.com/ruckbreasoning/numkbqa
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
https://github.com/thu-keg/probtree
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.