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Authors: Shengwu Xiong ; Xiaodong Wang ; Pengfei Duan ; Zhe Yu and Abdelghani Dahou

Affiliation: Wuhan University of Technology, China

Keyword(s): DAG Deep Knowledge Representation, Combinatory Categorial Grammar (CCG), Semantic Analysis.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Knowledge Engineering and Ontology Development ; Knowledge Representation and Reasoning ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems

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.

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Paper citation in several formats:
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 1: ICAART; ISBN 978-989-758-220-2; ISSN 2184-433X, SciTePress, pages 17-23. DOI: 10.5220/0006108900170023

@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 1: ICAART},
year={2017},
pages={17-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006108900170023},
isbn={978-989-758-220-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Deep Knowledge Representation based on Compositional Semantics for Chinese Geography
SN - 978-989-758-220-2
IS - 2184-433X
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
PB - SciTePress

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