We study the problem of computing an embedding of the tuples of a relational database in a manner that is extensible to dynamic changes of the database. In this problem, the embedding should be stable in the sense that it should not change on the existing tuples due to the embedding of newly inserted tuples (as database applications might already rely on existing embeddings); at the same time, the embedding of all tuples, old and new, should retain high quality. This task is challenging since inter-dependencies among the embeddings of different entities are inherent in state-of-the-art embedding techniques for structured data. We study two approaches to solving the problem. The first is an adaptation of Node2Vec to dynamic databases. The second is the FoRWaRD algorithm (Foreign Key Random Walk Embeddings for Relational Databases) that draws from embedding techniques for general graphs and knowledge graphs, and is inherently utilizing the schema and its key and foreign-key constraints. We evaluate the embedding algorithms using a collection of downstream tasks of column prediction over geographical and biological domains. We find that in the traditional static setting, our two embedding methods achieve comparable results that are compatible with the state-of-the-art for the specific applications. In the dynamic setting, we find that the FoRWaRD algorithm generally outperforms and runs faster than the alternatives, and moreover, it features only a mild reduction of quality even when the database consists of more than half newly inserted tuples after the initial training of the embedding.
翻译:我们研究如何以与数据库动态变化相适应的方式,将关系数据库的内嵌嵌成一个隐蔽的隐蔽体的问题。在此问题上,嵌入应保持稳定,因为它不应因嵌入新插入的图腾而改变现有的图腾(因为数据库应用程序可能已经依赖现有的嵌入体);同时,所有新旧图 ⁇ 的嵌入应保持高质量。这项任务具有挑战性,因为不同实体嵌入的内嵌体的相互依存性,甚至存在于结构化数据的最先进的内嵌技术中。我们研究解决这一问题的两种方法。首先,它不应因嵌入新插入的图腾(因为数据库应用程序可能已经依赖现有的嵌入系统);第二个是Forwarard算法(Forign Key Randow Embackings),它从嵌入一般图形和知识图的嵌入技术,它本身只利用结构化的内嵌式和外在结构上的限制。我们用新嵌入的算法来评估内嵌入的代算法,在常规和生物域域内嵌入的内嵌入中,我们通常在固定的内嵌入的内嵌入系统预测结果中,我们发现两种内嵌入的内存的内置的内置的内置的内置的内置结构,在比常规和内置的内置的内置的内置的内置的内置的内置的内置的内置。我们在地理和内置的内置的内置的内置的内置的内置的内置的内置方法,在地理和外的内置的内置的内置的内置的内置的内置的内置。