Digital data is a gold mine for modern journalism. However, datasets which interest
journalists are extremely heterogeneous, ranging from highly structured (relational …

In the era of Big Data, integrating information from multiple sources has proven valuable in
various fields. To ensure a high-quality supply of multi-source data, repairing different types …

Duplicate detection identifies multiple records in a dataset that represent the same real-
world object. Many such approaches exist, both in research and in industry. To investigate …

Real-world data obtained from integrating heterogeneous data sources are often multi-
valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy …

Entity resolution (ER) leads a key role in data preprocessing. ER identifies records
corresponding to the same real-world entity. Recent years have witnessed a growing trend …

Abstract In Papadakis et al.(2020), we presented the latest release of JedAI, an open-source
Entity Resolution (ER) system that allows for building a large variety of end-to-end ER …

Tables in scientific papers contain a wealth of valuable knowledge for the scientific
enterprise. To help the many of us who frequently consult this type of knowledge, we present …

Entity resolution aims to identify records that point to the same entity in the real world. In
recent years, crowdsourcing approaches have provided new ideas for entity resolution …

Data integration is an important step in any data science pipeline where the objective is to
unify the information available in different datasets for comprehensive analysis. Full …

Entity matching is a fundamental problem of data integration. It groups records according to
underlying real-world entities. There is a growing trend of entity matching via deep learning …