A dataset for connecting similar past and present causalities
2020
Ikejiri, Ryohei | Sumikawa, Yasunobu
In this data article, we present a dataset that includes past causalities and categories to connect similar past and present causalities. First, we collect past causalities by referencing certain well-known Japanese high-school textbooks. Subsequently, we select 138 causalities that are useful for analogizing from the causalities to considering solutions for confront present social issues. To enhance the analogy, we describe each causality in three contexts: background including problems, solution methods, and their results. We define 13 categories based on the selected causalities and Encyclopedia of Historiography. The past causalities belong to more than one category. In addition, to train machine learning models including classifier, we collect 900 past events from Wikipedia, and assign one or more categories to the past event data. We perform statistical analyses to understand the quality of the dataset. The proposed applications of the dataset include training machine learning models such as classifiers for past causalities and information retrieval for ranking present social issues according to the similarities between the present and past causalities.
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