In this paper, we address the problem of entity resolution, where given many references to underlying objects, the task is to predict which references correspond to the same object. We propose a probabilistic model for collective entity resolution. Our approach differs from other recently proposed entity resolution approaches in that it is a) unsupervised, b) generative and c) introduces a hidd...

This paper introduces and defines the concept of entity identity information management (EIIM). EIIM is a component of entity identity management (EIM) that utilizes data structures, data integration, and entity resolution (ER) methods and algorithms in order to maintain entity identity integrity, a goal that EIIM shares with master data management (MDM). The paper also explores some of the des...

For unsupervised clustering, traditional accuracy metrics based on the constituent records do not often reflect the accuracy at the cluster level. For a specific example, consider entity resolution where the goal is to cluster records across multiple, heterogeneous data sources into “entities.” Measuring the accuracy of entity resolution is not as simple as applying the well known record level ...

Entity resolution is the task of identifying all mentions that represent the same real-world entity within a knowledge base or across multiple knowledge bases. We address the problem of performing entity resolution on RDF graphs containing multiple types of nodes, using the links between instances of different types to improve the accuracy. For example, in a graph of products and manufacturers ...

This paper addresses the problem of entity identification in documents in which key identity attributes are missing. The most common approach is to take a single entity reference and determine the “best match” of its attributes to a set of candidate identities selected from an appropriate entity catalog. This paper describes a new technique of multiple-reference, shared-relationship identity re...

Entity resolution is the problem of finding co-referent instances, which at the same time describe the same topic. It is an important component of data integration systems and is indispensable in linked data publication process. Entity resolution has been a subject of extensive research; however, seeking for a perfect resolution algorithm remains a work in progress. Many approaches have been pr...

We present a query-time entity resolution process that works in a highly parallel fashion. We use the application MobEx to showcase our process, which consists of a mobile client and a server, where the server takes the role of a mediator and carries out the resolution. Results are propagated to the client as early as possible. Resolution results that are produced later in the process are send ...

Entity resolution is the process of matching records that refer to the same entities from one or several databases in situations where the records to be matched do not include unique entity identifiers. Matching therefore has to rely upon partially identifying information, such as names and addresses. Traditionally, entity resolution has been applied in batch-mode and on static databases. Howev...

We address the problem of ambiguity resolution in associating facts to the entities with multiple senses. A semi-supervised graphical model assigns the best categories for entities given the context. Problem Statement Analyzing the results of current algorithms for extracting facts about entities [1] shows that despite the mutual exclusion constraints that are enforced, there is still a need fo...

In this chapter we study the application of existing entity resolution (ER) techniques on a real-world multi-source genealogical dataset. Our goal is to identify all persons involved in various notary acts and link them to their birth, marriage and death certificates. We analyze the influence of additional ER features such as name popularity, geographical distance and co-reference information o...