Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and search. This chapter will discuss the specific problem of named ER in the context of personal knowledge graphs (PKGs). We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER. We also discuss some challenges that are expected to arise for Web-scale data. Next, we provide a brief literature review, with a special focus on how existing techniques can potentially apply to PKGs. We conclude the chapter by covering some applications, as well as promising directions for future research.

Sorted Neighborhood is an established blocking method for relational databases. It has not been applied on graph-based data models such as the Resource Description Framework (RDF). This poster presents a modular workflow for applying Sorted Neighborhood to RDF. Real-world evaluations demonstrate the workflow's utility against a popular baseline. Entity Resolution (ER) is the abstract problem of identifying Figure 1: A simple instance of ER in an RDF graph pairs of entities across databases that are syntactically disparate but logically equivalent. The problem goes by multiple names in the AI community, examples being record Table 1: Tuples sorted using blocking key values (BKVs) linkage, instance matching, and coreference resolution (Elmagarmid, ID First Name Last Name Zip BKV Ipeirotis, and Verykios 2007).