  Entity resolution is the process of identifying groups of records within or
across data sets where each group represents a real-world entity. Novel
techniques that consider temporal features to improve the quality of entity
resolution have recently attracted significant attention. However, there are
currently no large data sets available that contain both temporal information
as well as ground truth information to evaluate the quality of temporal entity
resolution approaches. In this paper, we describe the preparation of a temporal
data set based on author profiles extracted from the Digital Bibliography and
Library Project (DBLP). We completed missing links between publications and
author profiles in the DBLP data set using the DBLP public API. We then used
the Microsoft Academic Graph (MAG) to link temporal affiliation information for
DBLP authors. We selected around 80K (1%) of author profiles that cover 2
million (50%) publications using information in DBLP such as alternative author
names and personal web profile to improve the reliability of the resulting
ground truth, while at the same time keeping the data set challenging for
temporal entity resolution research.
