Abstract
Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts.
Highlights
Adequate specifications of qualitative commonsense information are indispensable to Naïve Geography, which formalizes common people’s knowledge about the geographic world [1], and serves as a mechanism of intelligent geographic information retrieval
They consist of one categorical variable that characterizes topological type (TC) based on the 9-intersection model, and other 18 metric variables including four variables characterizing splitting (IAS, Outer Area Splitting (OAS), Inner Traversal Splitting (ITS), and Perimeter Splitting (PS)), eight variables characterizing closeness (OAC, Outer Line Closeness (OLC), Inner Area Closeness (IAC), Inner Line Closeness (ILC), Outer Area Nearness (OAN), Outer Line Nearness (OLN), Inner Area Nearness (IAN), and Inner Line Nearness (ILN)), and six variables characterizing alongness (LA, Perimeter Alongness (PA), Inner Perimeter Alongness (IPA), Inner Line Alongness (ILA), Outer Perimeter Alongness (OPA), and Outer Line Alongness (OLA)) of line-region geometrical representations
fuzzy random forest (FRF) is a model built with FRF algorithm [30] and a classifier composed of multiple fuzzy decision trees (FDTs)
Summary
Adequate specifications of qualitative commonsense information are indispensable to Naïve Geography, which formalizes common people’s knowledge about the geographic world [1], and serves as a mechanism of intelligent geographic information retrieval. Du et al [7] used the random forest (RF) algorithm to build a mapping model between NLSR terms and topological and metric variables They are not robust in differentiating large number of NLSR terms
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