This paper explores “on-the-fly” data cleaning in the context of a user query. A novel Query-Driven Approach (QDA) is developed that performs a minimal number of cleaning steps that are only necessary to answer a given selection query correctly. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantage in terms of efficiency over traditional techniqu...

In recent years, crowdsourcing is increasingly applied as a means to enhance data quality. Although the crowd generates insightful information especially for complex problems such as entity resolution (ER), the output quality of crowd workers is often noisy. That is, workers may unintentionally generate false or contradicting data even for simple tasks. The challenge that we address in this pap...

We study the problem of enhancing Entity Resolution (ER) with the help of crowdsourcing. ER is the problem of clustering records that refer to the same real-world entity and can be an extremely di cult 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. We study the problem of resolv...

We present JedAI, a toolkit for Entity Resolution that can be used in three different ways: as an open-source Java library that implements numerous state-of-the-art, domain-independent methods, as a workbench that facilitates the evaluation of their relative performance and as a desktop application that offers out-of-the-box ER solutions. JedAI bridges the gap between the database and the Seman...

Nous nous intéressons dans cet article aux méthodes superficielles de résolution d’anaphores et de construction des chaı̂nes de référence, que nous avons développées comme modules du système d’extraction d’information ANNIE. La module ”orthomatcher” traite la coréférence orthographique des noms propres et le module de résolution d’anaphores traite les anaphores pronominales dont les antécédents ...

Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables. Our unsupervised system achieves 58.44% F1 score of the CoNLL metric on the English...

In this paper we propose an NP coreference resolution system which does resolution on the entity-level. The framework of the system is presented and different resolution strategies are investigated.

Resource Description Framework (RDF)1 is a data model that can be used to publish semistructured data visualized as directed graphs. An example is Dataset 1 in Fig. 1. Nodes in the graph represent entities and edges represent properties connecting these entities. Two nodes may refer to the same logical entity, despite being syntactically disparate. For example, the entity Mickey Beats in Datase...

The SERF project at Stanford deals with the Entity Resolution (ER) problem, in which records determined to represent the same real-life “entities” (such as people or products) are successively located and combined. The approach we pursue is “generic”, in the sense that the specific functions used to match and merge records are viewed as black boxes, which permits efficient, expressive and exten...

Entity Resolution (ER) is the process of identifying groups of records that refer to the same real-world entity. Various measures (e.g., pairwise F1, cluster F1) have been used for evaluating ER results. However, ER measures tend to be chosen in an ad-hoc fashion without careful thought as to what defines a good result for the specific application at hand. In this paper, our contributions are t...