Temporal Interval Reasoning with Korean Historical Event

In historical event knowledge base, relationships between time event intervals are complex that is not easy to express its complex relations. We make the time event intervals reasoning in order to express complicated relations among events in Korean historical event based on 13 Allen´s temporal interval relations, but it takes too much time to do the reasoning. In pre-computed model, if we have quantitative information in Korean history dataset, we pre-compute time event relations from possible pair of quantitative event intervals to qualitative event relation triples with Allen´s operator model. In this paper, we propose effective hybrid algorithm, which is a combination of pre-computed model and backward chaining to get a real time query and perform the reasoning more effectively between time event intervals in Korean history dataset with more than 3 billion triples. As user imposes questions in English, we reformulate it into qualitative structure query in which consists of Allen´s operators and then look up for answer in the existing qualitative answers that are already pre-computed. Otherwise, we infer only necessary entries from quantitative temporal information to compute the inferred facts to get the answer during query time based on backward chaining. We implemented this approach with a Spark Scala framework, which is a new parallel system programming that is capable of processing large-scale dataset efficiently and speeding up our reasoning process. With this reasoning process, we get a real time query with response times in a small number of milliseconds.