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<p>In this paper, we propose an approach to learn optimal service representations that can be used in downstream data mining tasks by combining attributes and multiple relations from a heterogeneous information network and using meta-paths to model service relations. We construct a heterogeneous information network that connects services, mashups, and their attributes and derive latent relational knowledge from it for representation learning. We address two major challenges related to using such a heterogeneous network in representation learning: (1) how to effectively combine attribute information and network topology, (2) how to optimize the weighting scheme among those inputs to enhance the learning from relevant features and minimize distraction from noisy ones. Our approach can take advantage of partially or fully annotated data, and still works in a fully unsupervised setting. We conduct a comprehensive experimental study on a real world dataset, where we use clustering as a downstream task. In our experiments, Our model performs better than all competing approaches.</p>

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Learning optimal heterogeneous service network representation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月23日

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  • Eduardo Lima,
  • Xumin Liu

摘要

In this paper, we propose an approach to learn optimal service representations that can be used in downstream data mining tasks by combining attributes and multiple relations from a heterogeneous information network and using meta-paths to model service relations. We construct a heterogeneous information network that connects services, mashups, and their attributes and derive latent relational knowledge from it for representation learning. We address two major challenges related to using such a heterogeneous network in representation learning: (1) how to effectively combine attribute information and network topology, (2) how to optimize the weighting scheme among those inputs to enhance the learning from relevant features and minimize distraction from noisy ones. Our approach can take advantage of partially or fully annotated data, and still works in a fully unsupervised setting. We conduct a comprehensive experimental study on a real world dataset, where we use clustering as a downstream task. In our experiments, Our model performs better than all competing approaches.