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
Most implemented papers
Neural Machine Translation for Query Construction and Composition
Research on question answering with knowledge base has recently seen an increasing use of deep architectures.
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
The most approaches to Knowledge Base Question Answering are based on semantic parsing.
Knowledge Base Question Answering via Encoding of Complex Query Graphs
Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task.
Interactive Instance-based Evaluation of Knowledge Base Question Answering
Most approaches to Knowledge Base Question Answering are based on semantic parsing.
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data.
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.
RuBQ: A Russian Dataset for Question Answering over Wikidata
The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset.
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive.
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases
To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64, 331 questions, GrailQA, and provide evaluation settings for all three levels of generalization.