Facilitating Data Discovery for Large-scale Science Facilities using Knowledge Networks
Facilitating Data Discovery for Large-scale Science Facilities using Knowledge Networks
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DOI：
10.1109/ipdps49936.2021.00073
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Yubo Qin;I. Rodero;M. Parashar
中科院分区：
文献类型：
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作者：
Yubo Qin;I. Rodero;M. Parashar
大型多用户科学设施，如地理分布的观测站，远程仪器和实验平台，代表了一些最大的国家投资，可以在许多科学领域取得巨大进步。这种进展的最近例子包括引力波的探测和黑洞事件视界的成像。然而，随着这些设施及其用户数量的增长，沿着其数据产品的复杂性，多样性和数量的增加，查找和访问相关数据变得越来越具有挑战性，限制了设施的潜在影响。随着科学家和应用程序工作流程越来越多地尝试整合来自不同领域的设施数据，这些挑战进一步放大。在本文中，我们利用概念的基础上推荐系统，这是非常有效的电子商务，以解决这些数据发现和数据访问的挑战，大规模分布式科学设施。我们首先分析数据从设施和识别和建模用户查询模式的设施位置和空间位置，特定领域的数据模型，和用户协会。然后，我们使用这种分析来生成知识图，并开发协作知识感知图注意力网络（CKAT）推荐模型，该模型利用图神经网络（GNN）通过传播显式编码协作信号，并将它们与知识关联联合收割机相结合。此外，我们整合了一个知识感知的神经注意力机制，使CKAT能够更多地关注关键信息，同时减少不相关的噪音，从而提高推荐的准确性。我们将所提出的模型应用于两个真实世界的设施数据集，并实证证明CKAT可以有效地促进数据发现，显着优于几个引人注目的最先进的基线模型。
Large-scale multiuser scientific facilities, such as geographically distributed observatories, remote instruments, and experimental platforms, represent some of the largest national investments and can enable dramatic advances across many areas of science. Recent examples of such advances include the detection of gravitational waves and the imaging of a black hole’s event horizon. However, as the number of such facilities and their users grow, along with the complexity, diversity, and volumes of their data products, finding and accessing relevant data is becoming increasingly challenging, limiting the potential impact of facilities. These challenges are further amplified as scientists and application workflows increasingly try to integrate facilities’ data from diverse domains. In this paper, we leverage concepts underlying recommender systems, which are extremely effective in e-commerce, to address these data-discovery and data-access challenges for large-scale distributed scientific facilities. We first analyze data from facilities and identify and model user-query patterns in terms of facility location and spatial localities, domain-specific data models, and user associations. We then use this analysis to generate a knowledge graph and develop the collaborative knowledge-aware graph attention network (CKAT) recommendation model, which leverages graph neural networks (GNNs) to explicitly encode the collaborative signals through propagation and combine them with knowledge associations. Moreover, we integrate a knowledge-aware neural attention mechanism to enable the CKAT to pay more attention to key information while reducing irrelevant noise, thereby increasing the accuracy of the recommendations. We apply the proposed model on two real-world facility datasets and empirically demonstrate that the CKAT can effectively facilitate data discovery, significantly outperforming several compelling state-of-the-art baseline models.