Few-Shot Learning-Based Lesser-Known POI Category Estimation Based on Syntactic and Semantic Information

Abstract

The estimation of points of interest (POI) categories is essential in several contexts, such as land use estimation, POI and itinerary recommendation in the tourism sector, and so on. Most of these approaches are based on well-known POIs and use information such as people’s mobility or check-in data. This information is not or rarely available for lesser-known POIs. However, these lesser-known POIs cannot be ignored because of this lack of information, as they may be important to local people in terms of their culture and history and worth discovering by tourists or local authorities. To address this challenge, we propose an approach based on the techniques of coupling the syntactic and semantic analysis of data via a knowledge graph using Few-shot learning (FSL) and Light Graph Convolution Network (LightGCN). The FSL technique allows us to work with very little data, which not only works with lesser-known POIs but also reduces the complexity in terms of tasks and execution time. The results show that our approach outperforms the baseline approaches and that considering the semantic aspect of the data via Linked Open Data (LOD) significantly improves the results of the approach based on the syntactic analysis alone.

Keywords

• POI category estimation
• few shot learning
• LightGCN
• syntactic and semantic analysis
• knowledge graph
• linked open data