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

arXiv: https://arxiv.org/abs/2311.12465 · HTML: https://mlsanity.com/paper/2311.12465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?](https://mlsanity.com/paper/1912.08320/) (2019)
- [The role of metadata in reproducible computational research](https://mlsanity.com/paper/2006.08589/) (2020)

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

- [From Knowledge Representation to Knowledge Organization and Back](https://mlsanity.com/paper/2312.07302/) (2023)
- [From Knowledge Organization to Knowledge Representation and Back](https://mlsanity.com/paper/2401.11753/) (2024)

## Abstract (arXiv)

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.