This tutorial brings together perspectives on ER from a variety of fields, including databases, machine learning, natural language processing and information retrieval, to provide, in one setting, a survey of a large body of work. We discuss both the practical aspects and theoretical underpinnings of ER. We describe existing solutions, current challenges, and open research problems.

Entity resolution (ER) is an important and common problem in data cleaning. It is about identifying and merging records in a database that represent the same real-world entity. Recently, matching dependencies (MDs) have been introduced and investigated as declarative rules that specify ER. An ER process induced by MDs over a dirty instance leads to multiple clean instances, in general. In this ...

Data quality is crucial in all information systems. As a key step in obtaining clean data, record linkage or entity resolution (ER) groups database records by the underlying real world entities. In this paper we give practical motivating examples and review the available ER formal models. The formal model for matching and merging records determines not just the power and quality, but also the a...

Previous research in cross-document entity coreference has generally been restricted to the offline scenario where the set of documents is provided in advance. As a consequence, the dominant approach is based on greedy agglomerative clustering techniques that utilize pairwise vector comparisons and thus require O(n2) space and time. In this paper we explore identifying coreferent entity mention...

An important prerequisite to successfully integrating protein data is detecting duplicate records spread across different databases. In this paper, we describe a new framework for protein entity resolution, called PERF, which deduplicates protein mentions using a wide range of protein attributes. A mention refers to any recorded information about a protein, whether it is derived from a database...

Identifying the different aliases used by or for an entity is emerging as a significant problem in reliable Information Extraction systems, especially with the proliferation of social media and their ever growing impact on different aspects of modern life such as politics, finance, security, etc. In this paper, we address the novel problem of Named Entity Aliasing Resolution (NEAR). We attempt ...

Some of the greatest advances in web search have come from leveraging socio-economic properties of online user behavior. Past advances include PageRank, anchor text, hubsauthorities, and TF-IDF. In this paper, we investigate another socio-economic property that, to our knowledge, has not yet been exploited: sites that create lists of entities, such as IMDB and Netflix, have an incentive to avoi...

Efficiently incorporating entity-level information is a challenge for coreference resolution systems due to the difficulty of exact inference over partitions. We describe an end-to-end discriminative probabilistic model for coreference that, along with standard pairwise features, enforces structural agreement constraints between specified properties of coreferent mentions. This model can be rep...

Recently, many advanced machine learning approaches have been proposed for coreference resolution; however, all of the discriminatively-trained models reason over mentions rather than entities. That is, they do not explicitly contain variables indicating the “canonical” values for each attribute of an entity (e.g., name, venue, title, etc.). This canonicalization step is typically implemented a...

This paper proposes a progressive approach to entity resolution (ER) that allows users to explore a trade-off between the resolution cost and the achieved quality of the resolved data. In particular, our approach aims to produce the highest quality result given a constraint on the resolution budget, specified by the user. Our proposed method monitors and dynamically reassesses the resolution pr...