How does XML handle metadata?

本文介绍了XML作为一种可定义自定义标记语言的技术如何被用来存储和链接各种格式的元数据,如ISO11179、Dublin Core等,并讨论了作者在实现元数据时的选择自由度。

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<script type="text/javascript" src="http://pagead2.googlesyndication.com/pagead/show_ads.js"> </script> Because XML lets you define your own markup language, you can make full use of the extended hypertext features (see the question on Links) of XML to store or link to metadata in any format (eg ISO 11179, Dublin Core, Warwick Framework, Resource Description Framework (RDF), and Platform for Internet Content Selection (PICS)). There are no predefined elements in XML, because it is an architecture, not an application, so it is not part of XML's job to specify how or if authors should or should not implement metadata. You are therefore free to use any suitable method from simple attributes to the embedding of entire Dublin Core/Warwick Framework metadata records. Browser makers may also have their own architectural recommendations or methods to propose.
### GraphML Format for Graph Neural Networks Usage and Information GraphML is an XML-based file format for graphs, which allows the storage of rich graph structures including nodes, edges, attributes, and hierarchical subgraphs. This format supports both directed and undirected graphs as well as hyperedges[^1]. For applications involving graph neural networks (GNNs), GraphML can serve multiple purposes. #### Structure Representation In GNN contexts, a typical GraphML document represents each node with its features and labels when applicable. Edges between these nodes are also detailed along with any associated weights or types that might influence message passing mechanisms within GNN architectures. ```xml <graphml xmlns="http://graphml.graphdrawing.org/xmlns"> <key id="d0" for="node" attr.name="label" attr.type="string"/> <key id="d1" for="edge" attr.name="weight" attr.type="double"/> <graph edgedefault="undirected"> <node id="n0"><data key="d0">A</data></node> <node id="n1"><data key="d0">B</data></node> <edge source="n0" target="n1"> <data key="d1">0.7</data> </edge> </graph> </graphml> ``` This example demonstrates how to define two connected nodes A and B with their respective identifiers (`id`) and labels stored under `attr.name`. The edge connecting them carries additional metadata such as weight through custom keys defined at the beginning of this snippet. #### Integration into GNN Frameworks To utilize GraphML files directly inside popular deep learning libraries like PyTorch Geometric or DGL requires conversion steps since native support may not exist out-of-the-box. Libraries often provide utilities for reading from various formats; however, specific implementations depend on library versions and updates over time. For instance, converting a GraphML structure into adjacency matrices suitable for feeding into convolutional layers involves parsing operations followed by constructing appropriate tensors representing connectivity patterns among entities described earlier in terms of vertices and links. --related questions-- 1. What tools or methods convert GraphML data efficiently for use in TensorFlow? 2. How does one handle large-scale GraphML datasets while training GNN models without running into memory issues? 3. Are there best practices regarding feature engineering specifically tailored towards preparing GraphML inputs for machine learning tasks? 4. Can you explain more about different ways to represent weighted edges during model development using GraphML formatted data?
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