Chronos: Facilitating History Discovery by Linking Temporal Records

Many data sets contain temporal records over a long period of time; each record is associated with a time stamp and describes some aspects of a realworld entity at that particular time. From such data, users often wish to search for entities in a particular period and understand the history of one entity or all entities in the data set. A major challenge for enabling such search and exploration is to identify records that describe the same realworld entity over a long period of time; however, linking temporal records is hard given that the values that describe an entity can evolve over time (e.g., a person can move from one affiliation to another). We demonstrate the CHRONOS system which offers users the useful tool for finding real-world entities over time and understanding history of entities in the bibliography domain. The core of CHRONOS is a temporal record-linkage algorithm, which is tolerant to value evolution over time. Our algorithm can obtain an F-measure of over 0.9 in linking author records and fix errors made by DBLP. We show how CHRONOS allows users to explore the history of authors, and how it helps users understand our linkage results by comparing our results with those of existing systems, highlighting differences in the results, explaining our decisions to users, and answering “what-if” questions.