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

TL;DR

This paper introduces LiveSchema, a gateway designed to aggregate, analyze, and embed knowledge graph schemas from various catalogs, facilitating easier data selection and manipulation for machine learning tasks.

Contribution

The paper presents LiveSchema, a novel platform that consolidates knowledge graph schemas, enabling querying, transformation, and embedding to support data-driven research.

Findings

Contains 1000 datasets from 4 sources

Supports dataset querying and transformation

Enables analysis and visualization of knowledge graphs

Abstract

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.