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 )

Most implemented papers

Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering

dengyang17/MTQA 6 Dec 2018

Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.

Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

hugochan/BAMnet NAACL 2019

When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles.

KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning


Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life.

A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering

xie-minghui/gfc Conference 2022

One of the key challenges of knowledge base question answering (KBQA) is the multi-hop reasoning.

Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

dki-lab/pangu 19 Dec 2022

Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects.

Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family

tan92hl/complex-question-answering-evaluation-of-gpt-family 14 Mar 2023

ChatGPT is a powerful large language model (LLM) that covers knowledge resources such as Wikipedia and supports natural language question answering using its own knowledge.

SPARQL as a Foreign Language

LiberAI/NSpM 25 Aug 2017

In the last years, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains.

AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data

ag-sc/AMUSE 26 Feb 2018

We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference.

Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

UKPLab/starsem2018-entity-linking SEMEVAL 2018

We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data.