Knowledge Base Question Answering
56 papers with code • 8 benchmarks • 13 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
FrenchMedMCQA
VANiLLa
Latest papers
Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering
We apply the grammar to knowledge base question answering, where the constraints by candidate expressions assist a semantic parser to generate valid KB elements.
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
Advancements in AI and natural language processing have revolutionized machine-human language interactions, with question answering (QA) systems playing a pivotal role.
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.
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
Based on preference data, DPA-RAG accomplishes both external and internal preference alignment: 1) It jointly integrate pair-wise, point-wise, and contrastive preference alignment abilities into the reranker, achieving external preference alignment among RAG components.
RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions
An essential requirement for a real-world Knowledge Base Question Answering (KBQA) system is the ability to detect the answerability of questions when generating logical forms.
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
To tackle these hurdles, we introduce Interactive-KBQA, a framework designed to generate logical forms through direct interaction with knowledge bases (KBs).
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11. 8% and 20. 7%, respectively.
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities.
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning
Additional experiments show that FuSIC-KBQA also outperforms SoTA KBQA models in the in-domain setting when training data is limited.
Benchmarking Geospatial Question Answering Engines using the Dataset GeoQuestions1089
We present the dataset GeoQuestions1089 for benchmarking geospatial question answering engines.
