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
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.
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing.
Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
Language models (LMs) have already demonstrated remarkable abilities in understanding and generating both natural and formal language.
Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs.
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users.
Question Decomposition Tree for Answering Complex Questions over Knowledge Bases
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA.
Knowledge Base Question Answering for Space Debris Queries
In this work we present a system, developed for the European Space Agency (ESA), that can answer complex natural language queries, to support engineers in accessing the information contained in a KB that models the orbital space debris environment.
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata
By pairing our semantic parser with GPT-3, we combine verifiable results with qualified GPT-3 guesses to provide useful answers to 96% of the questions in dev.
SRTK: A Toolkit for Semantic-relevant Subgraph Retrieval
In this paper, we present SRTK, a user-friendly toolkit for semantic-relevant subgraph retrieval from large-scale knowledge graphs.
Few-shot In-context Learning for Knowledge Base Question Answering
On GrailQA and WebQSP, our model is also on par with other fully-trained models.