Year: 2019
Type: preprint
Abstract: Developing new ideas and algorithms in the fields of graph processing and relational learning requires public datasets. While Wikidata is the largest open source knowledge graph, involving more than fifty million entities, it is larger than needed in many cases and even too large to be processed easily. Still, it is a goldmine of relevant facts and relations. Using this knowledge graph is time consuming and prone to task specific tuning which can affect reproducibility of results. Providing a unified framework to extract topic-specific subgraphs solves this problem and allows researchers to evaluate algorithms on common datasets. This paper presents various topic-specific subgraphs of Wikidata along with the generic Python code used to extract them. These datasets can help develop new methods of knowledge graph processing and relational learning.
Source: arXiv (Cornell University)
Authors: Boschin, Armand, Bonald, Thomas
Institution: Télécom Paris
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2949527323