2018
DOI: 10.3390/ijgi7060221
|View full text |Cite
|
Sign up to set email alerts
|

A Graph Database Model for Knowledge Extracted from Place Descriptions

Abstract: Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modelling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using a graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an exten… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
14
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
10

Relationship

0
10

Authors

Journals

citations
Cited by 23 publications
(14 citation statements)
references
References 64 publications
0
14
0
Order By: Relevance
“…Qualitative Spatial Reasoners (QSR), in turn, provide the means to answer more complex questions based on the description of external, that is, relational, qualities of places (cf. 'Place graphs'; Chen et al, 2018aChen et al, , 2018bHamzei et al, 2018;Kim et al, 2015Kim et al, , 2017aKim et al, , 2017bKremer, 2018). However, they focus on spatial qualities and have to deal with high computational complexity (cf., e.g., Fogliaroni, 2013).…”
Section: Comparison To Current Implementationsmentioning
confidence: 99%
“…Qualitative Spatial Reasoners (QSR), in turn, provide the means to answer more complex questions based on the description of external, that is, relational, qualities of places (cf. 'Place graphs'; Chen et al, 2018aChen et al, , 2018bHamzei et al, 2018;Kim et al, 2015Kim et al, , 2017aKim et al, , 2017bKremer, 2018). However, they focus on spatial qualities and have to deal with high computational complexity (cf., e.g., Fogliaroni, 2013).…”
Section: Comparison To Current Implementationsmentioning
confidence: 99%
“…Wang et al [66] propose a toponym recognition approach based on a deep belief network as a promising and powerful method to extract georeferenced information from text. Chen et al [67] propose a place graph model for modelling spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph through georeferencing, reasoning, and querying.…”
Section: Insights From the Special Issue On Place-based Gismentioning
confidence: 99%
“…Place descriptions in sources such as news articles, local chronicles, social media texts, and travel diaries represent a method of communicating spatial information and a type of mental representation of human spatial cognition [24,25]. By exploring human mobility and activities via place descriptions, we can extract abundant semantic information of places [26,27].…”
Section: Introductionmentioning
confidence: 99%