Frameworks for entity matching: A comparison

Köpcke, Hanna; Rahm, Erhard

doi:10.1016/j.datak.2009.10.003

Cited by 346 publications

(215 citation statements)

References 49 publications

Supporting

Mentioning

204

Contrasting

Unclassified

Order By: Relevance

“…Entity resolution is an active research topic and many approaches and frameworks have been developed and evaluated as described in recent surveys [6,15,3]. Current frameworks mostly support several methods for blocking, for matching, and for the combination of individual match results.…”

Section: Related Workmentioning

confidence: 99%

Parallel Entity Resolution with Dedoop

Kolb

Rahm

2012

Datenbank Spektrum

Self Cite

View full text Add to dashboard Cite

We provide an overview of Dedoop (Deduplication with Hadoop), a new tool for parallel entity resolution (ER) on cloud infrastructures. Dedoop supports a browserbased specification of complex ER strategies and provides a large library of blocking and matching approaches. To simplify the configuration of ER strategies with several similarity metrics, training-based machine learning approaches can be employed with Dedoop. Specified ER strategies are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. For improved performance, Dedoop supports redundancy-free multi-pass blocking as well as advanced load balancing approaches. To illustrate the usefulness of Dedoop, we present the results of a comparative evaluation of different ER strategies on a challenging real-world dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

Parallel Entity Resolution with Dedoop

Kolb

Rahm

2012

Datenbank Spektrum

Self Cite

View full text Add to dashboard Cite

show abstract

“…Record deduplication (also known as entity matching [21]) is a related research problem which concerns recognizing and discriminating ambiguous records/references in a database referring to a same underlying entity. These ambiguous references occur in databases due to the lack of unique identifier attributes.…”

Section: Related Workmentioning

confidence: 99%

Integration of scientific and social networks

et al. 2013

View full text Add to dashboard Cite

Abstract. In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks (i.e. The DBLP publication network and the Twitter social network). This task is a crucial step toward building a multi environment expert finding system that has recently attracted much attention in Information Retrieval community. In this paper, the problem of social and scientific network integration is divided into two sub problems. The first problem concerns finding those profiles in one network, which presumably have a corresponding profile in the other network and the second problem concerns the name disambiguation to find true matching profiles among some candidate profiles for matching. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. Because the labels of these candidate pairs are not independent, state-of-the-art classification methods such as logistic regression and decision tree, which classify each instance separately, are unsuitable for this task. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. Two main types of dependencies among candidate pairs are considered for designing the joint label prediction model which are quite intuitive and general. Using the discriminative approaches, we utilize various feature sets to train our proposed classifiers. An extensive set of experiments have been conducted on six test collection collected from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.

show abstract

“…Surveys [8,9]. review the various approaches, including named attributes computations [5], schema mapping [2,17] and duplicate detection in hierarchical data [10], all which inform the construction of profile linkage techniques.…”

Section: Record Linkage and Entity Resolutionmentioning

confidence: 99%

Online Social Network Profile Linkage

Zhang

Kan

Liu

et al. 2014

Information Retrieval Technology

View full text Add to dashboard Cite

Abstract. Piecing together social signals from people in different online social networks is key for downstream analytics. However, users may have different usernames in different social networks, making the linkage task difficult. To enable this, we explore a probabilistic approach that uses a domain-specific prior knowledge to address this problem of online social network user profile linkage. At scale, linkage approaches that are based on a naïve pairwise comparisons that have quadratic complexity become prohibitively expensive. Our proposed threshold-based canopying framework -named OPL -reduces this pairwise comparisons, and guarantees a upper bound theoretic linear complexity with respect to the dataset size. We evaluate our approaches on real-world, large-scale datasets obtained from Twitter and Linkedin. Our probabilistic classifier integrating prior knowledge into Naïve Bayes performs at over 85% F1-measure for pairwise linkage, comparable to state-of-the-art approaches.

show abstract

Frameworks for entity matching: A comparison

Cited by 346 publications

References 49 publications

Parallel Entity Resolution with Dedoop

Parallel Entity Resolution with Dedoop

Integration of scientific and social networks

Online Social Network Profile Linkage

Contact Info

Product

Resources

About