Acknowledgements Many people have assisted me in carrying out this project. Firstly I would like to thank my academic supervisors, Associate Professor Peter Christen and Dr. Qing Wang for their ideas, support, encouragement and feedback. I would also like to thank Dr. Paul Wong from the ANU Research Office for providing me with a place to work and helpful advice on the project itself and the SC...

The Entity Resolution problem has been widely addressed in the literature. In its simplest version, the problem takes as input a knowledge base composed of records describing real world entities and outputs the sets of records judged to correspond to the same real world entity. More elaborated versions take into account links amongst records representing relationships between the entities which...

OF THE DISSERTATION Exploring Entity Resolution for Multimedia Person Identification By Liyan Zhang Doctor of Philosophy in Computer Science University of California, Irvine, 2014 Professor Sharad Mehrotra, Chair The explosion of massive media data induced by the proliferation of digital cameras, mobile devices as well as the emergence of online media websites, has led us into the era of big da...

Entity resolution (ER) is the problem of identifying duplicate tuples, which are the tuples that represent the same real-world entity. There are many real-life applications in which the ER problem arises. These applications range from news aggregation websites, identifying the news that cover the same story, in order to avoid presenting one story several times to the user, to the integration of...

We study the problem of enhancing entity resolution (ER) with the help of crowdsourcing. ER is the problem of identifying records that refer to the same real-world entity and can be an extremely difficult process for computer algorithms alone. For example, figuring out which images refer to the same person can be a hard task for computers, but an easy one for humans. An important component of c...

Mention pair models that predict whether or not two mentions are coreferent have historically been very effective for coreference resolution, but do not make use of entity-level information. However, we show that the scores produced by such models can be aggregated to define powerful entity-level features between clusters of mentions. Using these features, we train an entity-centric coreference...

Databases often contain corrupted, degraded, and noisy data with duplicate entries across and within each database. Such problems arise in citations, medical databases, genetics, human rights databases, and a variety of other applied settings. The target of statistical inference can be viewed as an unsupervised problem of determining the edges of a bipartite graph that links the observed record...

In this paper we describe graph-based parallel algorithms for entity resolution that improve over the map-reduce approach. We compare two approaches to parallelize a Locality Sensitive Hashing (LSH) accelerated, Iterative Match-Merge (IMM) entity resolution technique: BCP, where records hashed together are compared at a single node/reducer, vs an alternative mechanism (RCP) where comparison loa...

We consider the Entity Resolution (ER) problem (also known as deduplication, or merge-purge), in which records determined to represent the same real-world entity are successively located and merged. Our approach to the ER problem is generic, in the sense that the functions for comparing and merging records are viewed as black-boxes. In this context, managing numerical confidences along with the...

Due to huge amount of inaccurate information and different types of ambiguity in the available digitized genealogical data, applying Entity Resolution techniques for determining the records referring to the same entity should be considered as the first and still very important step in analysis of this type of data. Traditional methods, use a standard string similarity measure to calculate the s...