Knowledge Base Question Answering Edit Task
Natural Language Processing • Question Answering
About
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
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Datasets
Greatest papers with code
SPARQL query generation for complex question answering with BERT and BiLSTM-based model
In this paper we describe question answering system for answering of complex questions over Wikidata knowledge base.
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
IJCNLP 2019
• INK-USC/KagNet
• 
Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life.
Ranked #3 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
COMMON SENSE REASONING KNOWLEDGE BASE QUESTION ANSWERING KNOWLEDGE GRAPHS NATURAL LANGUAGE INFERENCE
Neural Machine Translation for Query Construction and Composition
Research on question answering with knowledge base has recently seen an increasing use of deep architectures.
CODE GENERATION KNOWLEDGE BASE QUESTION ANSWERING SEMANTIC PARSING
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
COLING 2018
• UKPLab/coling2018-graph-neural-networks-question-answering
•
The most approaches to Knowledge Base Question Answering are based on semantic parsing.
Ranked #1 on
Knowledge Base Question Answering
on WebQSP-WD
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles.
INFORMATION RETRIEVAL KNOWLEDGE BASE QUESTION ANSWERING SEMANTIC PARSING
Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories
SEMEVAL 2018
• UKPLab/starsem2018-entity-linking
•
We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity.
Ranked #1 on
Entity Linking
on WebQSP-WD
ENTITY DISAMBIGUATION ENTITY LINKING KNOWLEDGE BASE QUESTION ANSWERING
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
ACL 2019
• wudapeng268/KBQA-Adapter
• 
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
• siat-nlp/TransDG
•
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.
Interactive Instance-based Evaluation of Knowledge Base Question Answering
Most approaches to Knowledge Base Question Answering are based on semantic parsing.
ENTITY LINKING KNOWLEDGE BASE QUESTION ANSWERING SEMANTIC PARSING