Matches in ESWC 2020 for { ?s ?p The paper presents a modular evaluation framework for graph embedding techniques. The paper starts with the definition of the graph embeddings and some introductory pages / related work which also includes an overview of the various related tasks. The evaluation framework is well-described, a UML diagram and example usage being included. The largest section of the paper includes an overview of the available tasks: classification, regression, clustering, entity relatedeness, document similarity and semantic analogies. The various tasks are well explained, datasets, structure, size, model, configuration, metric, ranges and interpretation or optimum being available. The evaluation is rather similar to a story and includes some details on available use cases. Overall, even though the evaluation can be improved upon, the work feels solid.". }
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- Paper.191_Review.0 hasContent "The paper presents a modular evaluation framework for graph embedding techniques. The paper starts with the definition of the graph embeddings and some introductory pages / related work which also includes an overview of the various related tasks. The evaluation framework is well-described, a UML diagram and example usage being included. The largest section of the paper includes an overview of the available tasks: classification, regression, clustering, entity relatedeness, document similarity and semantic analogies. The various tasks are well explained, datasets, structure, size, model, configuration, metric, ranges and interpretation or optimum being available. The evaluation is rather similar to a story and includes some details on available use cases. Overall, even though the evaluation can be improved upon, the work feels solid."".