The Semantic Web is distributed yet interoperable: Distributed since resources are created and published by a variety of producers, tailored to their specific needs and knowledge; Interoperable as entities are linked across resources, allowing to use resources from different providers in concord. Complementary to the explicit usage of Semantic Web resources, embedding methods made them applicable to machine learning tasks. Subsequently, embedding models for numerous tasks and structures have been developed, and embedding spaces for various resources have been published. The ecosystem of embedding spaces is distributed but not interoperable: Entity embeddings are not readily comparable across different spaces. To parallel the Web of Data with a Web of Embeddings, we must thus integrate available embedding spaces into a uniform space.

Current integration approaches are limited to two spaces and presume that both of them were embedded with the same method — both assumptions are unlikely to hold in the context of a Web of Embeddings. In this paper, we present FedCoder— an approach that integrates multiple embedding spaces via a latent space. We assert that linked entities have a similar representation in the latent space so that entities become comparable across embedding spaces. FedCoder employs an autoencoder to learn this latent space from linked as well as non-linked entities.

Our experiments show that FedCoder substantially outperforms state-of-the-art approaches when faced with different embedding models, that it scales better than previous methods in the number of embedding spaces, and that it improves with more graphs being integrated whilst performing comparably with current approaches that assumed joint learning of the embeddings and were, usually, limited to two sources. Our results demonstrate that FedCoder is well adapted to integrate the distributed, diverse, and large ecosystem of embeddings spaces into an interoperable Web of Embeddings.

语义网是分布式但可互操作的：由于资源由各种生产者创建和发布，根据他们的特定需求和知识量身定制，因此是分布式的；可互操作，因为实体跨资源链接，允许一致地使用来自不同提供商的资源。作为对语义 Web 资源的显式使用的补充，嵌入方法使其适用于机器学习任务。随后，已经开发了许多任务和结构的嵌入模型，并且已经发布了各种资源的嵌入空间。嵌入空间的生态系统是分布式的，但不可互操作：实体嵌入不容易在不同空间之间进行比较。为了将数据网络与嵌入网络并行，我们必须将可用的嵌入空间整合到一个统一的空间中。

当前的集成方法仅限于两个空间，并假设它们都嵌入了相同的方法——这两个假设都不太可能在嵌入网络的上下文中成立。在本文中，我们介绍了 FedCoder——一种通过潜在空间集成多个嵌入空间的方法。我们断言链接的实体在潜在空间中具有相似的表示，因此实体在嵌入空间中变得可比较。FedCoder 使用自动编码器从链接实体和非链接实体中学习这个潜在空间。

我们的实验表明，当面对不同的嵌入模型时，FedCoder 的性能大大优于最先进的方法，它在嵌入空间的数量上比以前的方法具有更好的扩展性，并且它可以通过集成更多的图来改进，同时与当前的性能相当假设联合学习嵌入的方法，通常仅限于两个来源。我们的结果表明，FedCoder 非常适合将分布式、多样化和大型嵌入空间生态系统集成到可互操作的嵌入网络中。