Discovering Latent Threads in Entity Histories

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.

IoT Data Quality

“…IoT data are often accompanied with data quality issues, during data generation, collection, transmission and parsing. Such dirty data obviously damage applications, e.g., fuel consumption prediction [12] or entity history discovery [10]. Improving IoT data quality however is particularly challenging, given the distinct features over the IoT data such as pervasive noises, unaligned timestamps, consecutive errors, misplaced columns, correlated errors, etc.…”