Intelligent Interaction with Virtual Geographical Environments Based on Geographic Knowledge Graph
| Autor: | Yan Ren, Feng Li, Bingchuan Jiang, Liheng Tan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Conditional random field
geographic knowledge graph (GeoKG) Computer science Geography Planning and Development lcsh:G1-922 02 engineering and technology computer.software_genre knowledge graph (KG) 020204 information systems 0202 electrical engineering electronic engineering information engineering Earth and Planetary Sciences (miscellaneous) Computers in Earth Sciences semantic conversion model Semantic Web virtual geographical environments (VGEs) Information retrieval Parsing Digital mapping business.industry Spatial database 020207 software engineering Construct (python library) Knowledge base question answering (QA) Core (graph theory) business computer lcsh:Geography (General) |
| Zdroj: | ISPRS International Journal of Geo-Information Volume 8 Issue 10 ISPRS International Journal of Geo-Information, Vol 8, Iss 10, p 428 (2019) |
| ISSN: | 2220-9964 |
| DOI: | 10.3390/ijgi8100428 |
| Popis: | 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 &ldquo knowledge&rdquo 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. |
| Databáze: | OpenAIRE |
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