LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs

2017 ◽  
pp. 210-218 ◽  
Author(s):  
Priyansh Trivedi ◽  
Gaurav Maheshwari ◽  
Mohnish Dubey ◽  
Jens Lehmann
Keyword(s):  
Question Answering ◽  
Knowledge Graphs ◽  
Complex Question

scholarly journals Message Passing for Complex Question Answering over Knowledge Graphs

2019 ◽  
Author(s):  
Svitlana Vakulenko ◽  
Javier David Fernandez Garcia ◽  
Axel Polleres ◽  
Maarten de Rijke ◽  
Michael Cochez
Keyword(s):  
Message Passing ◽  
Question Answering ◽  
Knowledge Graphs ◽  
Complex Question

scholarly journals Complex Question Answering on knowledge graphs using machine translation and multi-task learning

2021 ◽  
Author(s):  
Saurabh Srivastava ◽  
Mayur Patidar ◽  
Sudip Chowdhury ◽  
Puneet Agarwal ◽  
Indrajit Bhattacharya ◽  
...  
Keyword(s):  
Machine Translation ◽  
Question Answering ◽  
Task Learning ◽  
Knowledge Graphs ◽  
Complex Question

scholarly journals Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs

2019 ◽  
pp. 487-504 ◽  
Author(s):  
Gaurav Maheshwari ◽  
Priyansh Trivedi ◽  
Denis Lukovnikov ◽  
Nilesh Chakraborty ◽  
Asja Fischer ◽  
...  
Keyword(s):  
Question Answering ◽  
Learning To Rank ◽  
Knowledge Graphs ◽  
Complex Question

scholarly journals A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

2018 ◽  
Vol 10 (9) ◽  
pp. 3245 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Cheng Li ◽  
Meng Wang
Keyword(s):  
Artificial Intelligence ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Graph ◽  
Development History ◽  
One Belt One Road ◽  
History Of ◽  
Knowledge Graphs ◽  
The One ◽  
The Impact

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.

scholarly journals Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base

2020 ◽  
Author(s):  
Yongrui Chen ◽  
Huiying Li ◽  
Yuncheng Hua ◽  
Guilin Qi
Keyword(s):  
Knowledge Base ◽  
Structure Prediction ◽  
Question Answering ◽  
State Transition ◽  
Knowledge Bases ◽  
Second Stage ◽  
Transition Strategy ◽  
Query Structure ◽  
Two Stages ◽  
Complex Question

Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.

scholarly journals Question Answering on Scholarly Knowledge Graphs

2020 ◽  
pp. 19-32
Author(s):  
Mohamad Yaser Jaradeh ◽  
Markus Stocker ◽  
Sören Auer
Keyword(s):  
Question Answering ◽  
Knowledge Graphs

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Improving Question Answering over Knowledge Graphs Using Graph Summarization

2021 ◽  
pp. 489-500 ◽  
Author(s):  
Sirui Li ◽  
Kok Wai Wong ◽  
Chun Che Fung ◽  
Dengya Zhu
Keyword(s):  
Question Answering ◽  
Knowledge Graphs ◽  
Graph Summarization
Sign in / Sign up

Export Citation Format

Share Document