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Text GCN-SW-KNN: a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

机译：文本GCN-SW-KNN：通过考虑地理语义，用于WMS应用主题的新型协作培训多标签分类方法

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摘要

Without explicit description of map application themes, it is difficult for users to discover desired map resources from massive online Web Map Services (WMS). However, metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples, mixed vocabularies, variable length and content arbitrariness of text fields. In this paper, we propose a novel multi-label text classification method, Text GCN-SW-KNN, based on geographic semantics and collaborative training to improve classification accuracy. The semi-supervised collaborative training adopts two base models, i.e. a modified Text Graph Convolutional Network (Text GCN) by utilizing Semantic Web, named Text GCN-SW, and widely-used Multi-Label K-Nearest Neighbor (ML-KNN). Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text. The distances are calculated with the Semantic Web of Earth and Environmental Terminology (SWEET) and WordNet dictionaries. Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines, and demonstrate better stability in repeating experiments and robustness to less training data. Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.
机译：如果没有地图应用主题明确的说明，很难让用户从海量的在线Web地图服务（WMS）发现所需的地图资源。然而，基于元数据的地图应用程序的主题提取是一个具有挑战性的多标签文本分类任务由于有限的训练样本，混合词汇，可变长度和文本字段的内容任意。在本文中，我们提出了一个新颖的多标签文本分类方法，文本GCN-SW-KNN，基于地理语义和协同训练，提高分类精度。半监督协同训练，利用语义Web，命名为文本GCN-SW采用了两种基本型号，即修改的文本图形卷积网络（文字GCN），并广泛使用的多标签k近邻（ML-KNN）。文本GCN-SW从文本GCN通过在元数据文本的主题和单词间的最短距离的语义调整异构word文档图的邻接矩阵的提高。的距离与地球和环境术语（SWEET）和共发现字典的语义Web计算。在WMS和层的元数据显示两者，所提出的方法可达到较高的F1-得分和精度高于国家的最先进的基线，并且在重复的实验和鲁棒性较少训练数据表现出更好的稳定性实验。文字GCN-SW-KNN可以扩展到其他的多标签文本分类方案为更好地支持元数据增强和地理信息资源的发现在地球科学领域。

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