Knowledge Base Question Answering
68 papers with code • 10 benchmarks • 15 datasets
Knowledge Base Q&A is the task of answering questions from a knowledge base.
( Image credit: Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering )
Benchmarks
These leaderboards are used to track progress in Knowledge Base Question Answering
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Datasets
WebQuestions
SimpleQuestions
WebQuestionsSP
ComplexWebQuestions
LC-QuAD
FrenchMedMCQA
Most implemented papers
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
hugochan/BAMnet
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NAACL 2019
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles.
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
INK-USC/KagNet
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IJCNLP 2019
Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life.
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs
bahuia/hgnet
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The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph.
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
dki-lab/pangu
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Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects.
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family
tan92hl/complex-question-answering-evaluation-of-gpt-family
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ChatGPT is a powerful large language model (LLM) that covers knowledge resources such as Wikipedia and supports natural language question answering using its own knowledge.
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
SPINACH achieves a new state of the art on the QALD-7, QALD-9 Plus and QALD-10 datasets by 31. 0%, 27. 0%, and 10. 0% in $F_1$, respectively, and coming within 1. 6% of the fine-tuned LLaMA SOTA model on WikiWebQuestions.
SPARQL as a Foreign Language
In the last years, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains.
AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data
We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference.
