Deep Knowledge Representation based on Compositional Semantics for Chinese Geography

Shengwu Xiong, Xiaodong Wang, Pengfei Duan, Zhe Yu, Abdelghani Dahou

2017

Abstract

Elementary education resources for geography contain a wealth of knowledge that is a collection of information with various relationships. It is of vital importance to further develop human like intelligent technology for extracting deep semantic information to effectively understand the questions. 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.

Paper Citation

in Harvard Style

Xiong S., 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

in Bibtex Style

@conference{icaart17,
author={Shengwu Xiong and Xiaodong Wang and Pengfei Duan and Zhe Yu and Abdelghani Dahou},
title={Deep Knowledge Representation based on Compositional Semantics for Chinese Geography},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={17-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006108900170023},
isbn={978-989-758-220-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Deep Knowledge Representation based on Compositional Semantics for Chinese Geography
SN - 978-989-758-220-2
AU - Xiong S.
AU - Wang X.
AU - Duan P.
AU - Yu Z.
AU - Dahou A.
PY - 2017
SP - 17
EP - 23
DO - 10.5220/0006108900170023