Next Article in Journal
Strengths of Exaggerated Tsunami-Originated Placenames: Disaster Subculture in Sanriku Coast, Japan
Previous Article in Journal
A High-performance Cross-platform Map Rendering Engine for Mobile Geographic Information System (GIS)
Open AccessArticle

Intelligent Interaction with Virtual Geographical Environments Based on Geographic Knowledge Graph

1
Strategic Support Force Information Engineering University, Zhengzhou 450001, China
2
State Key Laboratory of Geo-information Engineering, Xi’an 750054, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 428; https://doi.org/10.3390/ijgi8100428
Received: 29 July 2019 / Revised: 4 September 2019 / Accepted: 16 September 2019 / Published: 24 September 2019
The core of intelligent virtual geographical environments (VGEs) is the formal expression of geographic knowledge. Its purpose is to transform the data, information, and scenes of a virtual geographic environment into “knowledge” that can be recognized by computer, so that the computer can understand the virtual geographic environment more easily. A geographic knowledge graph (GeoKG) is a large-scale semantic web that stores geographical knowledge in a structured form. Based on a geographic knowledge base and a geospatial database, intelligent interactions with virtual geographical environments can be realized by natural language question answering, entity links, and so on. In this paper, a knowledge-enhanced Virtual geographical environments service framework is proposed. We construct a multi-level semantic parsing model and an enhanced GeoKG for structured geographic information data, such as digital maps, 3D virtual scenes, and unstructured information data. Based on the GeoKG, we propose a bilateral LSTM-CRF (long short-term memory- conditional random field) model to achieve natural language question answering for VGEs and conduct experiments on the method. The results prove that the method of intelligent interaction based on the knowledge graph can bridge the distance between people and virtual environments. View Full-Text
Keywords: virtual geographical environments (VGEs); knowledge graph (KG); geographic knowledge graph (GeoKG); semantic conversion model; question answering (QA) virtual geographical environments (VGEs); knowledge graph (KG); geographic knowledge graph (GeoKG); semantic conversion model; question answering (QA)
Show Figures

Figure 1

MDPI and ACS Style

Jiang, B.; Tan, L.; Ren, Y.; Li, F. Intelligent Interaction with Virtual Geographical Environments Based on Geographic Knowledge Graph. ISPRS Int. J. Geo-Inf. 2019, 8, 428.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop