LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas
This addresses the problem of resource discovery and data manipulation for researchers working with knowledge graph schemas in ML, though it appears incremental as an early implementation.
The paper tackles the difficulty in accessing and manipulating knowledge graph schemas for machine learning by introducing LiveSchema, a gateway with services to facilitate their reuse, including an online catalog with over 800 resources.
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.