WikiDataSets: Standardized sub-graphs from Wikidata
This solves the issue of time-consuming and non-reproducible data preparation for researchers in graph processing and relational learning, though it is incremental as it builds on existing Wikidata infrastructure.
The paper tackles the problem of using the large and unwieldy Wikidata knowledge graph for research by providing standardized, topic-specific subgraphs, resulting in easier processing and improved reproducibility for algorithm evaluation.
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.