Title: A knowledge graph-based content selection model for data-driven text generation

Authors: Jun-Peng Gong; Juan Cao; Peng-Zhou Zhang

Addresses: Faculty of Science and Technology, Communication University of China, Beijing 100024, China ' New Media Institute, Communication University of China, Beijing 100024, China ' Faculty of Science and Technology, Communication University of China, Beijing 100024, China

Abstract: Content selection is a critical task for natural language generation. A novel approach based on knowledge graph is proposed. Structure data is mapping to the graph and combined with user defined knowledge. The model analyses the content selection features on the graph, and automatically learns the content selection rules. The model was evaluated in the domain of weather forecasting.

Keywords: content selection; ontology; knowledge graph; data-driven text generation; knowledge graph-based model; NLG.

DOI: 10.1504/IJRIS.2017.090049

International Journal of Reasoning-based Intelligent Systems, 2017 Vol.9 No.3/4, pp.205 - 209

Received: 14 Jun 2017
Accepted: 30 Jun 2017
Published online: 27 Feb 2018 *

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