Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing

• Published in: Frontiers in big data Vol. 4; p. 762899
• Main Authors: Wang, Dongjie, Liu, Kunpeng, Mohaisen, David, Wang, Pengyang, Lu, Chang-Tien, Fu, Yanjie
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 20.10.2021
• Subjects: Big Data; feature-topic pairing; semantic space; spatial graph; spatial representation learning; spatial space
• ISSN: 2624-909X; 2624-909X
• DOI: 10.3389/fdata.2021.762899

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.