Named Entity Resolution in Personal Knowledge Graphs

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.Comment: To appear as a book chapter by the same name in an upcoming (Oct. 2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and applications' edited by Tiwari et a

Sorted Neighborhood for the Semantic Web

Entity Resolution (ER) concerns identifying logically equivalent entity pairs across databases. To avoid quadratic pairwise comparisons of entities, blocking methods are used. 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