论文标题
敏锐的宇宙：知识图嵌入的生态系统，侧重于可重复性和可传递性
The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability
论文作者
论文摘要
将知识图（kg）嵌入在连续的矢量空间中的新兴趋势是为了将其用于机器学习任务。最近，已经提出了许多知识图嵌入（KGE）模型，这些模型在试图维持kgs的结构属性的同时，根据节点的相似性，取决于其对其他节点的边缘。 KGE可用于解决KG中的任务，例如新颖链接的预测和实体的歧义。它们也可以用于下游任务，例如问答和事实检查。总体而言，这些任务与语义网络社区有关。尽管它们很受欢迎，但KGE实验的可重复性以及提议的KGE模型向机器学习社区以外的研究领域的转移性可能是一个主要挑战。因此，我们提出了敏锐的宇宙，这是一个知识图嵌入的生态系统，我们已经开发出了重点是可重复性和可传递性。敏锐的宇宙目前由Python包装Pykeen（Python知识嵌入），Biokeen（生物知识嵌入）和敏锐的模型动物园组成，用于与社区共享训练有素的KGE模型。
There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs such as the similarity of nodes depending on their edges to other nodes. KGEs can be used to address tasks within KGs such as the prediction of novel links and the disambiguation of entities. They can also be used for downstream tasks like question answering and fact-checking. Overall, these tasks are relevant for the semantic web community. Despite their popularity, the reproducibility of KGE experiments and the transferability of proposed KGE models to research fields outside the machine learning community can be a major challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. The KEEN Universe currently consists of the Python packages PyKEEN (Python KnowlEdge EmbeddiNgs), BioKEEN (Biological KnowlEdge EmbeddiNgs), and the KEEN Model Zoo for sharing trained KGE models with the community.
