One of the significant barriers to the training of statistical models on
knowledge graphs is the difficulty that scientists have in finding the best
input data to address their prediction goal. In addition to this, a key
challenge is to determine how to manipulate these relational data, which are
often in the form of particular triples (i.e., subject, predicate, object), to
enable the learning process. Currently, many high-quality catalogs of knowledge
graphs, are available. However, their primary goal is the re-usability of these
resources, and their interconnection, in the context of the Semantic Web. This
paper describes the LiveSchema initiative, namely, a first version of a gateway
that has the main scope of leveraging the gold mine of data collected by many
existing catalogs collecting relational data like ontologies and knowledge
graphs. At the current state, LiveSchema contains - 1000 datasets from 4 main
sources and offers some key facilities, which allow to: i) evolving LiveSchema,
by aggregating other source catalogs and repositories as input sources; ii)
querying all the collected resources; iii) transforming each given dataset into
formal concept analysis matrices that enable analysis and visualization
services; iv) generating models and tensors from each given dataset.

LiveSchema 是一个旨在利用现有的本体论和知识图谱等关系数据资源的网关，并提供了聚合其他源目录和仓库、查询、转换为概念分析矩阵以及生成模型和张量的功能。

迈向知识图谱模式收集、分析和嵌入的网关

Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and  Embedding

One of the major barriers to the training of algorithms on knowledge graph
schemas, such as vocabularies or ontologies, is the difficulty that scientists
have in finding the best input resource to address the target prediction tasks.
In addition to this, a key challenge is to determine how to manipulate (and
embed) these data, which are often in the form of particular triples (i.e.,
subject, predicate, object), to enable the learning process. In this paper, we
describe the LiveSchema initiative, namely a gateway that offers a family of
services to easily access, analyze, transform and exploit knowledge graph
schemas, with the main goal of facilitating the reuse of these resources in
machine learning use cases. As an early implementation of the initiative, we
also advance an online catalog, which relies on more than 800 resources, with
the first set of example services.

该论文介绍了 LiveSchema 举措，即提供一系列服务以轻松访问、分析、转换和利用知识图谱模式，以促进机器学习方式下对这些资源的重复利用。