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
Latest papers with no code
Make a Choice! Knowledge Base Question Answering with In-Context Learning
Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB).
From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base
The parser uses the KoPL to generate the transparent logical forms.
Pay More Attention to Relation Exploration for Knowledge Base Question Answering
In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision.
Knowledge Base Question Answering: A Semantic Parsing Perspective
In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges.
Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question.
A Method of Query Graph Reranking for Knowledge Base Question Answering
This paper presents a novel reranking method to better choose the optimal query graph, a sub-graph of knowledge graph, to retrieve the answer for an input question in Knowledge Base Question Answering (KBQA).
Better Query Graph Selection for Knowledge Base Question Answering
This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA).
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked.
Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases
This allows us to spot those facts that failed to get retrieved from the KB and generate textual queries to extract them from the textual resources in an open-domain question answering fashion.
Learning to Transpile AMR into SPARQL
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
