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

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 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 Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity

2020 ◽  
Author(s):  
Yang Zhao ◽  
Lu Xiang ◽  
Junnan Zhu ◽  
Jiajun Zhang ◽  
Yu Zhou ◽  
...  
Keyword(s):  
Machine Translation ◽  
Knowledge Graph ◽  
Neural Machine Translation ◽  
Task Learning

scholarly journals Knowledge Graphs Enhanced Neural Machine Translation

2020 ◽  
Author(s):  
Yang Zhao ◽  
Jiajun Zhang ◽  
Yu Zhou ◽  
Chengqing Zong
Keyword(s):  
Machine Translation ◽  
Semantic Space ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Structured Information ◽  
Knowledge Graphs ◽  
Japanese Translation

Knowledge graphs (KGs) store much structured information on various entities, many of which are not covered by the parallel sentence pairs of neural machine translation (NMT). To improve the translation quality of these entities, in this paper we propose a novel KGs enhanced NMT method. Specifically, we first induce the new translation results of these entities by transforming the source and target KGs into a unified semantic space. We then generate adequate pseudo parallel sentence pairs that contain these induced entity pairs. Finally, NMT model is jointly trained by the original and pseudo sentence pairs. The extensive experiments on Chinese-to-English and Englishto-Japanese translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling the induced entities.

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
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