Blocking and Filtering Techniques for Entity Resolution

Papadakis, George; Skoutas, Dimitrios; Thanos, Emmanouil; Palpanas, Themis

doi:10.1145/3377455

Cited by 145 publications

(64 citation statements)

References 160 publications

(261 reference statements)

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“…These probabilistic methods are summarized by Herzog et al [38], Winkler [102,103]. Blocking, which is surveyed by Christen [16], Papadakis et al [72,73], is considered an important subtask of entity matching, meant to tackle the quadratic complexity of potential matches. Christophides et al [17] specifically review entity matching techniques in the context of big data.…”

Section: Other Surveys and Extensive Overviewsmentioning

confidence: 99%

Neural Networks for Entity Matching: A Survey

Barlaug¹,

Gulla²

2021

ACM Trans. Knowl. Discov. Data

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Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge. In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.

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Section: Other Surveys and Extensive Overviewsmentioning

confidence: 99%

Neural Networks for Entity Matching: A Survey

Barlaug¹,

Gulla²

2021

ACM Trans. Knowl. Discov. Data

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“…We now describe the traditional blocking algorithms that can be considered the standard and most frequently adopted approaches [7,26].…”

Section: State Of the Artmentioning

confidence: 99%

Blocking Techniques for Entity Linkage: A Semantics-Based Approach

et al. 2020

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Nowadays, data integration must often manage noisy data, also containing attribute values written in natural language such as product descriptions or book reviews. In the data integration process, Entity Linkage has the role of identifying records that contain information referring to the same object. Modern Entity Linkage methods, in order to reduce the dimension of the problem, partition the initial search space into “blocks” of records that can be considered similar according to some metrics, comparing then only the records belonging to the same block and thus greatly reducing the overall complexity of the algorithm. In this paper, we propose two automatic blocking strategies that, differently from the traditional methods, aim at capturing the semantic properties of data by means of recent deep learning frameworks. Both methods, in a first phase, exploit recent research on tuple and sentence embeddings to transform the database records into real-valued vectors; in a second phase, to arrange the tuples inside the blocks, one of them adopts approximate nearest neighbourhood algorithms, while the other one uses dimensionality reduction techniques combined with clustering algorithms. We train our blocking models on an external, independent corpus, and then, we directly apply them to new datasets in an unsupervised fashion. Our choice is motivated by the fact that, in most data integration scenarios, no training data are actually available. We tested our systems on six popular datasets and compared their performances against five traditional blocking algorithms. The test results demonstrated that our deep-learning-based blocking solutions outperform standard blocking algorithms, especially on textual and noisy data.

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“…Entity matching is generally computationally tricky because the number of possible matches is (| | × | |). Techniques for reducing the potential number of matches to be evaluated are often referred to by the common term blocking, and many effective and efficient techniques have been developed [9]. Even with these techniques, it can still be quite computationally heavy.…”

Section: Background and State Of The Artmentioning

confidence: 99%

Tailoring Entity Matching for Industrial Settings

Barlaug

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Entity matching has received significant attention from the research community over many years. Despite some limited success, most state-of-the-art methods see no widespread usage in industry. In this paper, we present the author's PhD research, which aims at identifying issues that hold techniques and methods developed by the research community back from use in industry, and look at how they might be adapted to address those issues. In our proposed approach, we implement a modular framework, which will be used for real-world user testing and quantitative experiments of our adapted methods. We will have three main contributions from our research: 1) We develop a modular framework for interactive entity matching combining intra-and inter-session iterations. 2) We show how active learning methods for entity matching can be adapted to learn not only classification of matches but also classification of which records are of interest to the user jointly, and how it compares to current methods. 3) We show how deep learning can be used to synthesize interpretable rules for entity matching, and how it compares to traditional methods.

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Blocking and Filtering Techniques for Entity Resolution

Cited by 145 publications

References 160 publications

Neural Networks for Entity Matching: A Survey

Neural Networks for Entity Matching: A Survey

Blocking Techniques for Entity Linkage: A Semantics-Based Approach

Tailoring Entity Matching for Industrial Settings

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