用于数据集成的Siamese图神经网络
Siamese Graph Neural Networks for Data Integration
摘要：
数据集成已经被广泛研究了几十年，并从不同的角度进行了探讨。然而，这个领域仍然主要是规则驱动的，缺乏通用的自动化。机器学习，特别是深度学习的最新发展为数据集成问题的更通用和更有效的解决方案开辟了道路。在这项工作中，我们提出了一个通用的方法来建模和集成实体的结构化数据，如关系数据库，以及非结构化的来源，如自由文本的新闻文章。我们的方法旨在明确建模和利用实体之间的关系，从而使用所有可用的信息，并保留尽可能多的上下文。这是通过结合siamese和图神经网络来实现的，以在连接的实体之间传播信息并支持高可扩展性。我们评估了我们的方法在整合业务实体数据的任务，我们证明，它优于标准的基于规则的系统，以及其他深度学习方法，不使用基于图形的表示。
英文摘要：
Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent development in machine learning and in particular deep learning has opened the way to more general and more efficient solutions to data integration problems. In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles. Our approach is designed to explicitly model and leverage relations between entities, thereby using all available information and preserving as much context as possible. This is achieved by combining siamese and graph neural networks to propagate information between connected entities and support high scalability. We evaluate our method on the task of integrating data about business entities, and we demonstrate that it outperforms standard rule-based systems, as well as other deep learning approaches that do not use graph-based representations.
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