Knowledge Base Question Answering
46 papers with code • 5 benchmarks • 9 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
WebQuestionsSP
ComplexWebQuestions
FrenchMedMCQA
VANiLLa
Most implemented papers
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
IBM/transition-amr-parser
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Findings (ACL) 2021
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases.
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals
In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network.
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering
lanyunshi/conversationalkbqa
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ACL 2021
We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking.
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
microsoft/KC
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ACL 2021
We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA).
Complex Knowledge Base Question Answering: A Survey
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB).
RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering
salesforce/rng-kbqa
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ACL 2022
We present RnG-KBQA, a Rank-and-Generate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability.
Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning
Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer.
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions.
