A Collective, Probabilistic Approach to Schema Mapping

Kimmig, Angelika; Memory, Alex; Miller, Renée J.; Getoor, Lise

doi:10.1109/icde.2017.140

Cited by 26 publications

(9 citation statements)

References 28 publications

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“…In addition, we show that CMD is effective on several problems with real data. This paper expands on our earlier work [20], proving complexity results, and contributing experimental results on additional real world problems as well as problems with an order of magnitude greater complexity. Section 2 illustrates the key challenges with an example.…”

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confidence: 80%

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

Kimmig

Memory

Miller

et al. 2019

IEEE Trans. Knowl. Data Eng.

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Abstract-We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise.

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confidence: 80%

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

Kimmig

Memory

Miller

et al. 2019

IEEE Trans. Knowl. Data Eng.

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“…Relation integration has been studied by both the database (DB) and the NLP communities. The DB community formulates it as schema matching that aligns the schemas of two tables, e.g., matching columns of an is in table to those of another subarea of table (Rahm and Bernstein, 2001;Cafarella et al, 2008;Kimmig et al, 2017). Such (Soderland et al, 2013), most works leverage the link structure between entities and relations.…”

Section: Related Workmentioning

confidence: 99%

CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction

Jiang¹,

Han²,

Sisman³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, previous methods exploit neighbor entities and relations for additional context. However, the predictions are made independently, which can be mutually inconsistent. We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. We further improve the collective model with augmented data from the portion of the target KG that is otherwise unused. Experiment results on two datasets show that CoRI can significantly outperform the baselines, improving AUC from .677 to .748 and from .716 to .780, respectively.

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“…However, in the context of data exchange it is made easier by having prescribed databases with known schemas. Other approaches learn mappings using data and metadata (schema) evidence [70] or using probabilistic inference over data and meta-data evidence that may be incomplete or inconsistent [41]. This has been complemented by important work on learning mappings from examples (see ten Cate et al [68] for a survey of these approaches) and many human-in-the-loop guided mapping refinement approaches.…”

Section: 'S Data Exchangementioning

confidence: 99%

Open data integration

Miller

2018

Proc. VLDB Endow.

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Open data plays a major role in supporting both governmental and organizational transparency. Many organizations are adopting Open Data Principles promising to make their open data complete, primary, and timely. These properties make this data tremendously valuable to data scientists. However, scientists generally do not have a priori knowledge about what data is available (its schema or content). Nevertheless, they want to be able to use open data and integrate it with other public or private data they are studying. Traditionally, data integration is done using a framework called query discovery where the main task is to discover a query (or transformation) that translates data from one form into another. The goal is to find the right operators to join, nest, group, link, and twist data into a desired form. We introduce a new paradigm for thinking about integration where the focus is on data discovery, but highly efficient internet-scale discovery that is driven by data analysis needs. We describe a research agenda and recent progress in developing scalable data-analysis or query-aware data discovery algorithms that provide high recall and accuracy over massive data repositories.

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A Collective, Probabilistic Approach to Schema Mapping

Cited by 26 publications

References 28 publications

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction

Open data integration

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