Showing papers by "Katsumi Tanaka published in 2019"
TL;DR: This study sheds new light on the characteristics of information about historical people recorded in the English Wikipedia and quantifies user interest in such data, and proposes a novel style of analysis in which signals derived from the hyperlink structure of Wikipedia as well as from article view logs are used.
Abstract: Wikipedia contains large amounts of content related to history. It is being used extensively for many knowledge intensive tasks within computer science, digital humanities and related fields. In this paper, we look into Wikipedia articles on historical people for studying link-related temporal features of articles on past people. Our study sheds new light on the characteristics of information about historical people recorded in the English Wikipedia and quantifies user interest in such data. We propose a novel style of analysis in which we use signals derived from the hyperlink structure of Wikipedia as well as from article view logs, and we overlay them over temporal dimension to understand relations between time periods, link structure and article popularity. In the latter part of the paper, we also demonstrate several ways for estimating person importance based on the temporal aspects of the link structure as well as a method for ranking cities using the computed importance scores of their related persons.
9 citations
01 Aug 2019
TL;DR: This paper introduces a novel task of history-based entity categorization taking a set of entity-related documents as an input and detects latent entity categories whose members share similar histories, effectively, grouping entities based on the similarities of their historical developments.
Abstract: Knowledge of entity histories is often necessary for comprehensive understanding and characterization of entities. In this paper we introduce a novel task of history-based entity categorization. Taking a set of entity-related documents as an input we detect latent entity categories whose members share similar histories, effectively, grouping entities based on the similarities of their historical developments. Next, we generate comparative timelines for each generated group allowing users to spot similarities and differences in entity histories. We evaluate our approach on several datasets of different entity types demonstrating its effectiveness against competitive baselines.
2 citations
TL;DR: A novel task of history-based entity categorization and comparison is described, which determines latent entity categories whose members share similar histories and generates comparative timelines allowing users to elucidate similarities and differences in the histories of entities.
Abstract: Knowledge of entity histories is often necessary for comprehensive understanding and characterization of entities. Yet, the analysis of an entity’s history is often most meaningful when carried out in comparison with the histories of other entities. In this paper, we describe a novel task of history-based entity categorization and comparison. Based on a set of entity-related documents which are assumed as an input, we determine latent entity categories whose members share similar histories; hence, we are effectively grouping entities based on the correspondences in their historical developments. Next, we generate comparative timelines for each determined group allowing users to elucidate similarities and differences in the histories of entities. We evaluate our approach on several datasets of different entity types demonstrating its effectiveness against competitive baselines.
2 citations