Deep Knowledge Representation based on Compositional Semantics for Chinese Geography
Published in The 9th International Conference on Agents and Artificial Intelligence ICAART, 2017
In this paper, we propose a novel directed acyclic graph (DAG) deep knowledge representation built upon the theorem of combinational semantics. Knowledge is decomposed into nodes and edges which are then inserted into the ontology knowledge base. Experimental results demonstrate the superiority of the proposed method on question answering, especially when the syntax of question is complex, and its representation is fuzzy.
Recommended citation: Xiong, S.; Wang, X.; Wang, X.; Duan, P.; Yu, Z. and Dahou, A. (2017). Deep Knowledge Representation based on Compositional Semantics for Chinese Geography.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 17-23. DOI: 10.5220/0006108900170023