Machine Learning for Efficient Integration of Record Systems for Missing US Service Members More than 16 million Americans served in World War II. Of these service members, over 400,000 were killed in action during the war. Today, more than 72,000 service members remain unaccounted for from World War II. The United States continues to diligently locate, recover, and identify missing personnel from World War II and other past conflicts to provide the fullest possible accounting. This work importantly provides closure and resolution to numerous US families. To fulfill this mission, massive amounts of information must be integrated from historical records, genealogy records, anthropological data, archeological data, odontology data, and DNA. These disparate data sources are produced and maintained by multiple agencies, with different data governance rules and different internal structuring of service member information. Previously, a manual approach had been undertaken to Extract, Transform, Load (ETL) records from these different data sources, which creates the potential for introduced human error. In addition, a large number of personhours were required to synthesize this data on a biweekly basis. To address this issue, we implemented (i) a regex decision tree to translate genealogical relationships into DNA type availability and (ii) a machine learning approach for recordlinkage between disparate data sources. This application is currently in production and greatly reduces personhours needed and has a very low error rate for record translation and integration.

A Novel Record Linkage Interface That Incorporates Group Structure to Rapidly Collect Richer Labels Linking historical data longitudinally allows researchers to better characterize topics like population mobility, the impact of local  national events, and generational changes. The ideal linking process would involve subject matter experts with detailed information about each record, including any relationships to other records, however, this indepth process is expensive and often infeasible. Record linkage is the process of identifying and labeling records corresponding to unique entities. These statistical models largely rely on pairwise comparisons, underutilizing information about group structure and historical knowledge. Moreover, model performance can be limited by using labels of unknown certainty or origin. In record linkage, we are rarely given information about the number of labelers, how often they agreed, or the labeling process itself. Understanding how and why records are linked together for the dual purposes of gaining insights into the human decisionmaking process and improving record linkage models is an exciting, high impact area of research. We present an interactive labeling interface for use at the initial stages of the (potentially crowdsourced) record linkage process. The interface captures labeled records while tracking the labeler actions. The interface allows labelers to view and interact with the records at both the individual and group level, thereby providing nested labels. We simultaneously receive information about the label certainty and the labeleru0027s decisionmaking process via repeated label instances and clickstreams. We demonstrate the utility of this interface on the recently released, unlabeled 1901 and 1911 Ireland Census records and discuss the benefits of richer labels.