2019 Fiscal Year Annual Research Report
Novel Technologies for Improving Comprehension and Utilization of Historical Knowledge
| Project/Area Number |
17H01828
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| Research Institution | Kyoto University |
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Principal Investigator |
Adam Jatowt 京都大学, 情報学研究科, 特定准教授 (00415861)
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| Co-Investigator(Kenkyū-buntansha) |
澄川 靖信 首都大学東京, 大学教育センター, 助教 (70756303)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Keywords | digital archives / computational history / temporal information |
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| Outline of Annual Research Achievements |
Grouping is a common technique used for organizing and understanding entities. Yet, historical aspects are often quite important as, in many cases, the history shapes and defines the present characteristics of entities. We have proposed methods for grouping entities based on their histories. To enable history-based entity grouping, we formulate the latent history-based category hypothesis, which states that entities can be categorized based on the similarity of their histories, such that entities included in the same category have more similar histories to each other than to ones in other categories. In order to group entities based on their histories, we propose a concise optimization model inspired by the popular Affinity Propagation (AP) algorithm for exemplar-based clustering.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The planned project was done on time.
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| Strategy for Future Research Activity |
In the future we would like to continue evaluation of the proposed categorization research methods. We also would like to extend them to created history-focused summaries of entity categories which share similar histories.
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