Hierarchical Query Graph Generation for Complex Question Answering over Knowledge Graph

2020 ◽  
Author(s):  
Yunqi Qiu ◽  
Kun Zhang ◽  
Yuanzhuo Wang ◽  
Xiaolong Jin ◽  
Long Bai ◽  
...  
Keyword(s):  
Question Answering ◽  
Knowledge Graph ◽  
Query Graph ◽  
Complex Question ◽  
Graph Generation

scholarly journals Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

2015 ◽  
Author(s):  
Wen-tau Yih ◽  
Ming-Wei Chang ◽  
Xiaodong He ◽  
Jianfeng Gao
Keyword(s):  
Knowledge Base ◽  
Question Answering ◽  
Semantic Parsing ◽  
Query Graph ◽  
Graph Generation

scholarly journals Question-aware memory network for multi-hop question answering in human–robot interaction

2021 ◽  
Author(s):  
Xinmeng Li ◽  
Mamoun Alazab ◽  
Qian Li ◽  
Keping Yu ◽  
Quanjun Yin
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Human Robot Interaction ◽  
Knowledge Graph ◽  
Robot Interaction ◽  
Natural Language Question ◽  
Memory Network ◽  
The Given ◽  
Fine Tune ◽  
Language Question

AbstractKnowledge graph question answering is an important technology in intelligent human–robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates the effectiveness of our model.

scholarly journals Complex Factoid Question Answering with a Free-Text Knowledge Graph

2020 ◽  
Author(s):  
Chen Zhao ◽  
Chenyan Xiong ◽  
Xin Qian ◽  
Jordan Boyd-Graber
Keyword(s):  
Question Answering ◽  
Knowledge Graph ◽  
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