Developing new ideas and algorithms in the fields of graph processing and relational learning requires datasets to work with and WikiData is the largest open source knowledge graph involving more than fifty millions entities. It is larger than needed in many cases and even too large to be processed easily but it is still a goldmine of relevant facts and subgraphs. Using this 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.

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