Given the document below, you have to determine if "Yes" or "No", the summary is factually consistent with the document.

Document:
In this work, we address the semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem with the advanced graph convolution in a conventional supervised manner, but the performance could be heavily affected when labeled data is scarce. Here we propose a Graph Inference Learning (GIL) framework to boost the performance of NAME_1 classification by learning the inference of NAME_1 labels on graph topology. To bridge the connection of two nodes, we formally define a structure relation by encapsulating NAME_1 attributes, between-NAME_1 paths and local topological structures together, which can make inference conveniently deduced from one NAME_1 to another NAME_1. For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted into test nodes. Comprehensive evaluations on four benchmark datasets (including NAME_2, Citeseer, Pubmed and NELL) demonstrate the superiority of our GIL when compared with other state-of-the-art methods in the semi-supervised NAME_1 classification task.

Summary:
1. We propose a novel graph inference learning framework by building structure relations to infer unknown NAME_1 labels from those labeled clus Please reply with exactly 7 paragraphs and separate each paragraph with two new lines. The very 3rd paragraph must start with the word "monitoring".
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