Domain- and Structure-Agnostic End-to-End Entity Resolution with JedAI

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

doi:10.1145/3385658.3385664

Cited by 37 publications

(7 citation statements)

References 27 publications

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“…In addition we show the best reported result found in related work from matching systems using supervised learning while matching systems that use other types of learning (e.g. active learning or semisupervised learning) are excluded from this comparison [13,18,22]. The interpretation of the comparison to the results reported in related work should be made with attention to the differing, unfixed train, optimization, and test sets.…”

Section: Baseline Resultsmentioning

confidence: 94%

“…For the cases where the complete mapping is provided, non-matching pairs can be generated by calculating the Cartesian product of all records and excluding the matching pairs. Given the size of the data sets, this often results in large numbers of non-matching pairs and thus motivates the usage of blocking techniques [8,18] to remove obvious non-matches which are not helpful for training and uninteresting for testing. As the benchmark tasks only define matches, different researchers who use these tasks, generate different sets of nonmatches which influence the model training [2,16,17].…”

Section: Benchmark Tasksmentioning

confidence: 99%

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Profiling Entity Matching Benchmark Tasks

Primpeli

Bizer

2020

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

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Entity matching is a central task in data integration which has been researched for decades. Over this time, a wide range of benchmark tasks for evaluating entity matching methods has been developed. This resource paper systematically complements, profiles, and compares 21 entity matching benchmark tasks. In order to better understand the specific challenges associated with different tasks, we define a set of profiling dimensions which capture central aspects of the matching tasks. Using these dimensions, we create groups of benchmark tasks having similar characteristics. Afterwards, we assess the difficulty of the tasks in each group by computing baseline evaluation results using standard feature engineering together with two common classification methods. In order to enable the exact reproducibility of evaluation results, matching tasks need to contain exactly defined sets of matching and non-matching record pairs, as well as a fixed development and test split. As this is not the case for some widely-used benchmark tasks, we complement these tasks with fixed sets of non-matching pairs, as well as fixed splits, and provide the resulting development and test sets for public download. By profiling and complementing the benchmark tasks, we support researchers to select challenging as well as diverse tasks and to compare matching systems on clearly defined grounds.

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Section: Baseline Resultsmentioning

confidence: 94%

Section: Benchmark Tasksmentioning

confidence: 99%

Profiling Entity Matching Benchmark Tasks

Primpeli

Bizer

2020

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

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“…Note that there is a trade-off between the efficiency and the effectiveness of these two approaches [48]: step-by-step configuration is typically much faster, as it gradually minimizes the computational cost of every workflow step. In contrast, holistic configuration might involve a workflow step with high computational cost, as long as the overall F-Measure is high.…”

Section: Auxiliary Componentsmentioning

confidence: 99%

“…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

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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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“…There are cases, however, where many of the entities in these general-interest KBs are irrelevant for certain applications, therefore domain-specific ontologies for semantic information brokering, based on inter-ontology relationships such as synonyms, hyponyms, and hypernyms of the extracted entities are used [56]. In order to further increase the links between morphologically dissimilar extracted entities and KB-related objects, neural-based methods are also implemented, exploiting word embeddings to represent semantic spaces [57,58], also allowing for domain-agnostic entity resolution [59]. With regard to sentiment analysis (neutral vs. emotionally loaded) and polarity (positive vs. negative) detection of a text [60], lexicon-based [61], ML-based [62], and neural-based [63] classifiers are commonly used to identify the polarity of a relation within a sentence.…”

Section: Advances In Entity Linking Enrichment and Representationmentioning

confidence: 99%

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

2020

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The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject–predicate–object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks.

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Domain- and Structure-Agnostic End-to-End Entity Resolution with JedAI

Cited by 37 publications

References 27 publications

Profiling Entity Matching Benchmark Tasks

Profiling Entity Matching Benchmark Tasks

Three-dimensional Entity Resolution with JedAI

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

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