of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity

not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of "organization," "person," or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, indicating

Entity resolution is the problem of determining which records in a database refer to the same entities, and is a crucial and expensive step in the data mining process. Interest in it has grown rapidly in recent years, and many approaches have been proposed. However, they tend to address only

Entity resolution is central to data integration and data cleaning. Algorithmic approaches have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a more accurate but expensive (and slow) way to bring human insight into the process. Previous work has proposed

variable. We propose a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account. Additionally, we do not assume the domain of entities to be known and show how to infer the number of entities from the data. We demonstrate the utility

Entity resolution (ER), also known as duplicate detection or record matching, is the prob-lem of identifying the tuples that represent the same real world entity. In this paper, we address the problem of ER for uncertain data, which we call ERUD. We propose two dif-ferent approaches for the ERUD

We consider the Entity Resolution (ER) problem (also known as deduplication, or merge-purge), in which records determined to represent the same realworld entity are successively located and merged. Our approach to the ER problem is generic, in the sense that the functions for comparing and merging

of the performance trends predicted by our analysis of collective entity resolution over a wide range of structural characteristics in the data. 1.

Entity resolution (ER) identifies database records that refer to the same real world entity. In practice, ER is not a one-time process, but is constantly improved as the data, schema and application are better understood. We address the problem of keeping the ER result up-to-date when the ER logic

that measures the degree of interconnectedness between various pairs of nodes in the graph can significantly improve the quality of entity resolution. Furthermore, the paper presents an algorithm for making that technique self-adaptive to the underlying data, thus minimizing the required participation from