Novel Entity Discovery from Web Tables

Zhang, Shuo; Meij, Edgar; Balog, Krisztian; Reinanda, Ridho

doi:10.1145/3366423.3380205

Cited by 37 publications

(41 citation statements)

References 29 publications

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“…The ODE layer in the generator transforms ⊕ , the concatenation of a noisy vector and a condition vector , into another latent vector ′ that will be fed into the generator (See Section 3.3). reasons, many web-oriented researchers focus on various tasks on tabular data [10,12,27,30,32,45,56,59,62,63]. In this work, generating realistic synthetic tabular data is of our utmost interest.…”

Section: ) (C)mentioning

confidence: 99%

OCT-GAN: Neural ODE-based Conditional Tabular GANs

Kim

Jeon

Lee

et al. 2021

Proceedings of the Web Conference 2021

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Synthesizing tabular data is attracting much attention these days for various purposes. With sophisticate synthetic data, for instance, one can augment its training data. For the past couple of years, tabular data synthesis techniques have been greatly improved. Recent work made progress to address many problems in synthesizing tabular data, such as the imbalanced distribution and multimodality problems. However, the data utility of state-of-the-art methods is not satisfactory yet. In this work, we significantly improve the utility by designing our generator and discriminator based on neural ordinary differential equations (NODEs). After showing that NODEs have theoretically preferred characteristics for generating tabular data, we introduce our designs. The NODE-based discriminator performs a hidden vector evolution trajectory-based classification rather than classifying with a hidden vector at the last layer only. Our generator also adopts an ODE layer at the very beginning of its architecture to transform its initial input vector (i.e., the concatenation of a noisy vector and a condition vector in our case) onto another latent vector space suitable for the generation process. We conduct experiments with 13 datasets, including but not limited to insurance fraud detection, online news article prediction, and so on, and our presented method outperforms other state-of-the-art tabular data synthesis methods in many cases of our classification, regression, and clustering experiments. CCS CONCEPTS• Computing methodologies → Machine learning; Neural networks.

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Section: ) (C)mentioning

confidence: 99%

OCT-GAN: Neural ODE-based Conditional Tabular GANs

Kim

Jeon

Lee

et al. 2021

Proceedings of the Web Conference 2021

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“…We follow FAIR (Findable, Accessible, Interoperable and Reusable) guidelines to release our contributions. 14 We release our dataset in Zenodo (DOI: https://doi.org/10.5281/zenodo. 3840646), in such a way that researchers in the community can benefit from this.…”

Section: Availability and Long-term Planmentioning

confidence: 99%

“…A recent study reported that many of the approaches tested on such datasets are focused on "obviously linkable" cells [14], showing that a tool like T2K [10] manages to match only 2.85% of a large corpus of Web tables to DBpedia. However, the performance of T2K evaluated on T2D relatively to the CEA task is very high (F1: 0.82, Precision: 0.90, Recall: 0.76).…”

Section: Introductionmentioning

confidence: 99%

Tough Tables: Carefully Evaluating Entity Linking for Tabular Data

Cutrona

Bianchi

Jiménez-Ruiz

et al. 2020

Lecture Notes in Computer Science

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Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables). Last year, the SemTab challenge was introduced to unify different efforts to evaluate table annotation algorithms by providing a common interface and several general-purpose datasets as a ground truth. The SemTab dataset is useful to have a general understanding of how these algorithms work, and the organizers of the challenge included some artificial noise to the data to make the annotation trickier. However, it is hard to analyze specific aspects in an automatic way. For example, the ambiguity of names at the entity-level can largely affect the quality of the annotation. In this paper, we propose a novel dataset to complement the datasets proposed by SemTab. The dataset consists of a set of highquality manually-curated tables with non-obviously linkable cells, i.e., where values are ambiguous names, typos, and misspelled entity names not appearing in the current version of the SemTab dataset. These challenges are particularly relevant for the ingestion of structured legacy sources into existing knowledge graphs. Evaluations run on this dataset show that ambiguity is a key problem for entity linking algorithms and encourage a promising direction for future work in the field.

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“…Additionally, knowledge graphs are typically incomplete -entities and types mentioned in tables may not always exist in the knowledge graph. Therefore, it becomes necessary to expand the knowledge graph with new entities (Zhang et al, 2020) and types for annotating tables.…”

Section: Introductionmentioning

confidence: 99%

“…More recent approaches using embeddings (Gentile et al, 2017;Zhang and Balog, 2018;Zhang et al, 2019;Chen et al, 2019;Yin et al, 2020) only partly capture the syntactic structure of tables, and also ignore the structure of the knowledge graph. The problem of incompleteness of the knowledge representation (Zhang et al, 2020) is mostly not addressed.…”

Section: Introductionmentioning

confidence: 99%

Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network

Pramanick¹,

Bhattacharya²

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure. We propose TabGCN, which uses Graph Convolutional Networks to capture the complete structure of tables, knowledge graph and the training annotations, and jointly learns embeddings for table elements as well as the entities and types. To account for knowledge incompleteness, TabGCN's embeddings can be used to discover new entities and types. Using experiments on 5 benchmark datasets, we show that TabGCN significantly outperforms multiple state-of-the-art baselines for table annotation, while showing promising performance on downstream table-related applications.

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Novel Entity Discovery from Web Tables

Cited by 37 publications

References 29 publications

OCT-GAN: Neural ODE-based Conditional Tabular GANs

OCT-GAN: Neural ODE-based Conditional Tabular GANs

Tough Tables: Carefully Evaluating Entity Linking for Tabular Data

Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network

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