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Discovery Projects - Grant ID: DP160101934

Discovery Projects - Grant ID: DP160101934
ARC | Discovery Projects
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  • Developing a Temporal Bibliographic Data Set for Entity Resolution

    Hu, Yichen; Wang, Qing; Christen, Peter (2018)
    Projects: ARC | Discovery Projects - Grant ID: DP160101934 (DP160101934)
    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, w...

    Application of Advanced Record Linkage Techniques for Complex Population Reconstruction

    Record linkage is the process of identifying records that refer to the same entities from several databases. This process is challenging because commonly no unique entity identifiers are available. Linkage therefore has to rely on partially identifying attributes, such as names and addresses of people. Recent years have seen the development of novel techniques for linking data from diverse application areas, where a major focus has been on linking complex data that contain records about diffe...

    Skyblocking: Learning Blocking Schemes on the Skyline

    Shao, Jingyu; Wang, Qing; Lin, Yu (2018)
    Projects: ARC | Discovery Projects - Grant ID: DP160101934 (DP160101934)
    In this paper, for the first time, we introduce the concept of skyblocking, which aims to learn scheme skylines. Given a set of blocking schemes and measures (e.g. PC and PQ), each scheme can be mapped as a point to a scheme space where each measure is one dimension. The scheme skyline points are not dominated by any other scheme point in the scheme space considering their measure values. The main difference with traditional skyline queries is that the measure values associated with the block...

    Scalable Multi-Database Privacy-Preserving Record Linkage using Counting Bloom Filters

    Privacy-preserving record linkage (PPRL) aims at integrating sensitive information from multiple disparate databases of different organizations. PPRL approaches are increasingly required in real-world application areas such as healthcare, national security, and business. Previous approaches have mostly focused on linking only two databases as well as the use of a dedicated linkage unit. Scaling PPRL to more databases (multi-party PPRL) is an open challenge since privacy threats as well as the...
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