JedAI: The Force Behind Entity Resolution

Papadakis, George; Tsekouras, Leonidas; Thanos, Emmanouil; Giannakopoulos, George; Palpanas, Themis; Koubarakis, Manolis

doi:10.1007/978-3-319-70407-4_30

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…In this paper, we propose the NA-BLOCKER technique, which is capable of tolerating noisy data, extracting information regarding the schema of the data sources, generating groups of similar attributes, and pruning the generated blocking results in order to enhance the quality of the final blocks. Since Web approaches need to deal with data sources that present noisy and heterogeneous data, the proposed technique can be useful for these approaches, such as LIMES [20], LOV [28] and JedAI [24]. Based on the experimental results, we can highlight that NA-BLOCKER presents better results regarding effectiveness and aggregate cardinality than the state-of-the-art technique.…”

Section: Discussionmentioning

confidence: 97%

A noise tolerant and schema-agnostic blocking technique for entity resolution

Araújo

Pires

Mestre

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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The increasing use of Web systems has become a valuable source of semi-structured data. In this context, the Entity Resolution (ER) task emerges as a fundamental step to integrate multiple knowledge bases or identify similarities between the data items (i.e., entities). Usually, blocking techniques are widely applied as an initial step of ER approaches in order to avoid computing similarities between all pairs of entities (quadratic cost). In practice, heterogeneous and noisy data increase the difficulties faced by blocking techniques, since these issues directly interfere the block generation. To address these challenges, we propose the NA-BLOCKER technique, which is capable of tolerating noisy data to extract information regarding the data schema and generate high-quality blocks. NA-BLOCKER applies Locality Sensitive Hashing (LSH) to hash the attribute values of entities and enable the generation of high-quality blocks, even with the presence of noise in the attribute values. In our experimental evaluation, we use five real-world datasets, and highlight that NA-BLOCKER presents better results regarding effectiveness compared to the state-of-the-art technique. In terms of efficiency, NA-BLOCKER produces, on average, 34% less comparisons. However, due to the cost introduced by LSH, it results in an increase of the execution time at around 30%, on average.

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Section: Discussionmentioning

confidence: 97%

A noise tolerant and schema-agnostic blocking technique for entity resolution

Araújo

Pires

Mestre

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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“…Entity resolution, also known as record deduplication and entity matching, aims to identify records referencing the same real-world entity in one or multiple datasets [1,6]. ER solutions address blocking problems [7][8][9][10][11], rule matching problems [12][13][14][15], crowdsourcing [16][17][18], or machine learning problems [3][4][5]19]. Good overviews of ER can be found in surveys such as [20,21].…”

Section: Related Workmentioning

confidence: 99%

“…The pyramid-like structure consists of several hidden layers, with the bottom layer having the most hidden units, and each subsequent layer having half the number of hidden units as the previous layer. Specifically, the vector representations for sentence s and sentence l are defined as shown in (8).…”

Section: [ ] [ ] [ ] Cls Att Val Att Val Sep Att Val Att Val Sepmentioning

confidence: 99%

Enhancing entity resolution with multichannel BERT: a comprehensive approach

Geng

2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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“…Note that JedAI has already been presented in a short journal paper [48] and as a demo in past conferences [12,84,85]. The first releases, i.e., version 1 [84], version 2 [12] and version 2.1 [48], cover exclusively the serialized execution of the budget-and schema-agnostic workflow that is presented in Section 4.1, while providing a rather limited experimental analysis of its performance [48]. The serialized implementation of the batch schema-based workflow and of the budget-and schemaagnostic workflow are briefly presented in [85], without evaluating their relative performance.…”

Section: Blockingmentioning

confidence: 99%

Three-dimensional Entity Resolution with JedAI

Papadakis

Mandilaras

Gagliardelli

et al. 2020

Information Systems

Self Cite

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Entity Resolution (ER) is the task of detecting different entity profiles that describe the same real-world objects. To facilitate its execution, we have developed JedAI, an open-source system that puts together a series of state-of-the-art ER techniques that have been proposed and examined independently, targeting parts of the ER end-to-end pipeline. This is a unique approach, as no other ER tool brings together so many established techniques. Instead, most ER tools merely convey a few techniques, those primarily developed by their creators. In addition to democratizing ER techniques, JedAI goes beyond the other ER tools by offering a series of unique characteristics: (i) It allows for building and benchmarking millions of ER pipelines. (ii) It is the only ER system that applies seamlessly to any combination of structured and/or semi-structured data. (iii) It constitutes the only ER system that runs seamlessly both on stand-alone computers and clusters of computers-through the parallel implementation of all algorithms in Apache Spark. (iv) It supports two different end-to-end workflows for carrying out batch ER (i.e., budget-agnostic), a schema-agnostic one based on blocks, and a schema-based one relying on similarity joins. (v) It adapts both end-to-end workflows to budget-aware (i.e., progressive) ER. We present in detail all features of JedAI, stressing the core characteristics that enhance its usability, and boost its versatility and effectiveness. We also compare it to the state-of-the-art in the field, qualitatively and quantitatively, demonstrating its state-of-the-art performance over a variety of large-scale datasets from different domains.

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JedAI: The Force Behind Entity Resolution

Cited by 7 publications

References 3 publications

A noise tolerant and schema-agnostic blocking technique for entity resolution

A noise tolerant and schema-agnostic blocking technique for entity resolution

Enhancing entity resolution with multichannel BERT: a comprehensive approach

Three-dimensional Entity Resolution with JedAI

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