TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables

Fetahu, Besnik; Anand, Avishek; Koutraki, Maria

doi:10.1145/3308558.3313629

Cited by 29 publications

(28 citation statements)

References 25 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…As a result, relational tables with this pattern are preferred, which might therefore result in lower coverage. TableNet (Fetahu et al, 2019), a recent study on the interlinking of tables with subPartOf and equivalent relations, can provide a better understanding of table patterns. In the future, it would be desirable if an automatic query intent classifier were to identify the type of result table sought, which does not need to be limited to relational tables.…”

Section: Table Searchmentioning

confidence: 99%

Web Table Extraction, Retrieval and Augmentation

Zhang

Balog

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represent a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks : table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.

show abstract

“…This results in table pair candidates for joins or unions, but does not have a way to canonicalize entities or detect duplicates. Similarly, in the recent work of Fetahu et al [7], pairs of Wikipedia tables with equivalent or subsumed schemata are discovered with a neural network classifier, and this is useful for improving the detection of complex relations. None of these works integrate the tables with a KB, nor distinguish pivoted cells from values in the table headers.…”

Section: Related Workmentioning

confidence: 99%

Extracting N-ary Facts from Wikipedia Table Clusters

Kruit

Boncz

Urbani

2020

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

View full text Add to dashboard Cite

Tables in Wikipedia articles contain a wealth of knowledge that would be useful for many applications if it were structured in a more coherent, queryable form. An important problem is that many of such tables contain the same type of knowledge, but have different layouts and/or schemata. Moreover, some tables refer to entities that we can link to Knowledge Bases (KBs), while others do not. Finally, some tables express entity-attribute relations, while others contain more complex n-ary relations. We propose a novel knowledge extraction technique that tackles these problems. Our method first transforms and clusters similar tables into fewer unified ones to overcome the problem of table diversity. Then, the unified tables are linked to the KB so that knowledge about popular entities propagates to the unpopular ones. Finally, our method applies a technique that relies on functional dependencies to judiciously interpret the table and extract n-ary relations. Our experiments over 1.5M Wikipedia tables show that our clustering can group many semantically similar tables. This leads to the extraction of many novel n-ary relations.

show abstract

TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables

Cited by 29 publications

References 25 publications

OCT-GAN: Neural ODE-based Conditional Tabular GANs

OCT-GAN: Neural ODE-based Conditional Tabular GANs

Web Table Extraction, Retrieval and Augmentation

Extracting N-ary Facts from Wikipedia Table Clusters

Contact Info

Product

Resources

About