Generalized supervised meta-blocking

Information Systems, 2013

Entity resolution is the process of discovering groups of tuples that correspond to the same real-world entity. Blocking algorithms separate tuples into blocks that are likely to contain matching pairs. Tuning is a major challenge in the blocking process and in particular, high expertise is needed in contemporary blocking algorithms to construct a blocking key, based on which tuples are assigned to blocks. In this work, we introduce a blocking approach that avoids selecting a blocking key altogether, relieving the user from this difficult task. The approach is based on maximal frequent itemsets selection, allowing early evaluation of block quality based on the overall commonality of its members. A unique feature of the proposed algorithm is the use of prior knowledge of the estimated size of duplicate sets in enhancing the blocking accuracy. We report on a thorough empirical analysis, using common benchmarks of both real-world and synthetic datasets to exhibit the effectiveness and efficiency of our approach.

Strategic Innovative Marketing, 2017

In the Web of data, entities are described by interlinked data rather than documents on the Web. In this talk, we focus on entity resolution in the Web of data, i.e., on the problem of identifying descriptions that refer to the same real-world entity within one or across knowledge bases in the Web of data. To reduce the required number of pairwise comparisons among descriptions, methods for entity resolution typically perform a pre-processing step, called blocking, which places similar entity descriptions into blocks and executes comparisons only between descriptions within the same block. The objective of this talk is to present challenges and algorithms for blocking for entity resolution, stemming from the Web openness in describing, by an unbounded number of KBs, a multitude of entity types across domains, as well as the high heterogeneity (semantic and structural) of descriptions, even for the same types of entities.

Information Systems, 2017

Entity resolution constitutes a crucial task for many applications, but has an inherently quadratic complexity. In order to enable entity resolution to scale to large volumes of data, blocking is typically employed: it clusters similar entities into (overlapping) blocks so that it suffices to perform comparisons only within each block. To further increase efficiency, Meta-blocking is being used to clean the overlapping blocks from unnecessary comparisons, increasing precision by orders of magnitude at a small cost in recall. Despite its high time efficiency though, using Meta-blocking in practice to solve entity resolution problem on very large datasets is still challenging: applying it to 7.4 million entities takes (almost) 8 full days on a modern high-end server. In this paper, we introduce scalable algorithms for Meta-blocking, exploiting the MapReduce framework. Specifically, we describe a strategy for parallel execution that explicitly targets the core concept of Meta-blocking, the blocking graph. Furthermore, we propose two more advanced strategies, aiming to reduce the overhead of data exchange. The comparison-based strategy creates the blocking graph implicitly, while the entity-based strategy is independent of the blocking graph, employing fewer MapReduce jobs with a more elaborate processing. We also introduce a load balancing algorithm that distributes the computationally intensive workload evenly among the available compute nodes. Our experimental analysis verifies the feasibility and superiority of our advanced strategies, and demonstrates their scalability to very large datasets.

Knowledge and Information Systems, 2019

The problem of identifying duplicated entities in a dataset has gained increasing importance during the last decades. Due to the large size of the datasets, this problem can be very costly to be solved due to its intrinsic quadratic complexity. Both researchers and practitioners have developed a variety of techniques aiming to speed up a solution to this problem. One of these techniques is called blocking, an indexing technique that splits the dataset into a set of blocks, such that each block contains entities that share a common property evaluated by a blocking key function. In order to improve the efficacy of the blocking technique, multiple blocking keys may be used, and thus, a set of blocking results is generated. In this paper, we investigate how to control the size of the blocks generated by the use of multiple blocking keys and maintain reasonable quality results, which is measured by the quality of the produced blocks. By controlling the size of the blocks, we can reduce the overall cost of solving an entity resolution problem and facilitate the execution of a variety of tasks (e.g., real-time and privacy-preserving entity resolution). For doing so, we propose many heuristics which exploit the co-occurrence of entities among the generated blocks for pruning, splitting and merging blocks. The experimental results we carry out using four datasets confirm the adequacy of the proposed heuristics for generating block sizes within a predefined range threshold as well as maintaining reasonable blocking quality results.

Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11, 2011

We have recently witnessed an enormous growth in the volume of structured and semi-structured data sets available on the Web. An important prerequisite for using and combining such data sets is the detection and merge of information that describes the same real-world entities, a task known as Entity Resolution. To make this quadratic task efficient, blocking techniques are typically employed. However, the high dynamics, loose schema binding, and heterogeneity of (semi-)structured data, impose new challenges to entity resolution. Existing blocking approaches become inapplicable because they rely on the homogeneity of the considered data and a-priory known schemata. In this paper, we introduce a novel approach for entity resolution, scaling it up for large, noisy, and heterogeneous information spaces. It combines an attribute-agnostic mechanism for building blocks with intelligent block processing techniques that boost blocks with high expected utility, propagate knowledge about identified matches, and preempt the resolution process when it gets too expensive. Our extensive evaluation on real-world, large, heterogeneous data sets verifies that the suggested approach is both effective and efficient.

2020

JedAI is an open-source toolkit that allows for building and benchmarking thousands of schema-agnostic Entity Resolution (ER) pipelines through a non-learning, blocking-based end-to-end workflow. In this paper, we present its latest release, JedAI3, which conveys two new end-to-end workflows: one for budgetagnostic ER that is based on similarity joins, and one for budgetaware (i.e., progressive) ER. This version also adds support for pre-trained word or character embeddings and connects JedAI to the Python data analysis ecosystem. Overall, these enhancements provide JedAI with features offered by no other ER tool, especially in the schemaand domain-agnostic context.

International Journal of Approximate Reasoning

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language LogiQL-an extended form of Datalog supported by the LogicBlox platform-for all activities related to data processing, and the specification and enforcement of MDs.

International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No. 3, June 2024, 2024

Data integration is a major challenge in the era of big data analytics. Inaccurate integration can lead to incorrect analysis results. Entity resolution, which identifies similar entities across different data sources, is a crucial step in the integration process. Existing blocking techniques used to group similar entities before the matching step often neglect semantic criteria, resulting in reduced blocking quality. To address this, a new blocking architecture is proposed in this paper. The architecture incorporates a semantic similarity layer using natural language processing and deep learning techniques. The architecture is schema-agnostic and treats datasets as unstructured records to improve accuracy. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed architecture in terms of accuracy, reduction ratio, and F-measure.

IEEE Transactions on Big Data, 2016

An increasing number of entities are described by interlinked data rather than documents on the Web. Entity Resolution (ER) aims to identify descriptions of the same real-world entity within one or across knowledge bases in the Web of data. To reduce the required number of pairwise comparisons among descriptions, ER methods typically perform a pre-processing step, called blocking, which places similar entity descriptions into blocks and thus only compare descriptions within the same block. We experimentally evaluate several blocking methods proposed for the Web of data using real datasets, whose characteristics significantly impact their effectiveness and efficiency. The proposed experimental evaluation framework allows us to better understand the characteristics of the missed matching entity descriptions and contrast them with ground truth obtained from different kinds of relatedness links.

Proceedings of the 35th SIGMOD international conference on Management of data - SIGMOD '09, 2009

Entity Resolution (ER) is the problem of identifying which records in a database refer to the same real-world entity. An exhaustive ER process involves computing the similarities between pairs of records, which can be very expensive for large datasets. Various blocking techniques can be used to enhance the performance of ER by dividing the records into blocks in multiple ways and only comparing records within the same block. However, most blocking techniques process blocks separately and do not exploit the results of other ...