LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas

TL;DR

LiveSchema is a gateway that simplifies access, analysis, and manipulation of knowledge graph schemas to enhance their reuse in machine learning tasks, addressing key challenges in embedding and resource selection.

Contribution

It introduces the LiveSchema gateway and an online catalog with over 800 resources, providing tools to facilitate schema reuse in machine learning.

Findings

Developed a comprehensive online catalog of knowledge graph schemas.

Provided initial services for analyzing and transforming schemas.

Enabled easier integration of schemas into machine learning workflows.

Abstract

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.