2018
EMNLP
EMNLP 2018
Knowledge Base Question Answering via Encoding of Complex Query Graphs
Abstract
AbstractAnswering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Knowledge & Reasoning and Machine Learning and Natural Language Processing
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Trend Setter
— Question Answering
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Keyword Pioneer
— complex query
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Natural Language Processing > Applications > Question Answering
Knowledge & Reasoning > Representation > Knowledge Graphs
Artificial Intelligence > Core AI > Knowledge Graph
Deep Learning > Learning Types > Representation Learning
Deep Learning > Techniques > Representation Learning
Artificial Intelligence > Core AI > Question Answering