# LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas

arXiv: https://arxiv.org/abs/2207.06112 · HTML: https://mlsanity.com/paper/2207.06112/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [The role of metadata in reproducible computational research](https://mlsanity.com/paper/2006.08589/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Diversity-Aware Domain Development Methodology](https://mlsanity.com/paper/2208.13064/) (2022)
- [Knowledge Graph Extension by Entity Type Recognition](https://mlsanity.com/paper/2405.02463/) (2024)

## Abstract (arXiv)

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.