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 )
Datasets
Latest papers with no code
RETINAQA : A Knowledge Base Question Answering Model Robust to both Answerable and Unanswerable Questions
We propose a new model for KBQA named RetinaQA that is robust against unaswerability.
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
For each category of complex question, we devised exemplars to guide LLMs through the reasoning processes.
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.
Clue-Guided Path Exploration: An Efficient Knowledge Base Question-Answering Framework with Low Computational Resource Consumption
In this paper, we introduce a Clue-Guided Path Exploration framework (CGPE) that efficiently merges a knowledge base with an LLM, placing less stringent requirements on the model's capabilities.
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning
Additional experiments in the in-domain setting show that FuSIC-KBQA also outperforms SoTA KBQA models when training data is limited.
In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models
However, generating the most appropriate knowledge base query code based on Natural Language Questions (NLQ) poses a significant challenge in KBQA.
An In-Context Schema Understanding Method for Knowledge Base Question Answering
The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base.
ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources
With the introduction of deep learning models, semantic parsingbased knowledge base question answering (KBQA) systems have achieved high performance in handling complex questions.
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries.
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering
The generalization problem on KBQA has drawn considerable attention.