Large-scale Entity Extraction and Probabilistic Record Linkage

Large-scale entity extraction, disambiguation and linkage in Big Data can challenge the traditional methodologies developed over the last three decades. Entity linkage, in particular, is cornerstone for a wide spectrum of applications, such as Master Data Management, Data Warehousing, Social Graph Analytics, Fraud Detection and Identity Management. Traditional rules based heuristic methods usually don't scale properly, are language specific and require significant maintenance over time. This presentation will introduce the audience to the use of probabilistic record linkage, also known as specificity based linkage, on Big Data, to perform language independent large-scale entity extraction, resolution and linkage across diverse sources. The presentation also includes a live demonstration reviewing the different steps required during the data integration process (ingestion, profiling, parsing, cleansing, standardization and normalization), and show the basic concepts behind probabilistic record linkage on a real-world application using the open source big data platform, HPCC Systems [1] from LexisNexis.