DeepAI AI Chat
Log In Sign Up

Learning Semantic Annotations for Tabular Data

05/30/2019
by   Jiaoyan Chen, et al.
0

The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table's contextual semantics, including table locality features learned by a Hybrid Neural Network (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm.It exhibits good performance not only on individual table sets, but also when transferring from one table set to another.

READ FULL TEXT

page 1

page 2

page 3

page 4

11/04/2018

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

Automatically annotating column types with knowledge base (KB) concepts ...
09/11/2021

Making Table Understanding Work in Practice

Understanding the semantics of tables at scale is crucial for tasks like...
09/20/2019

Automatic Table completion using Knowledge Base

Table is a popular data format to organize and present relational inform...
03/19/2015

Syntagma Lexical Database

This paper discusses the structure of Syntagma's Lexical Database (focus...
09/28/2022

Revealing the Semantics of Data Wrangling Scripts With COMANTICS

Data workers usually seek to understand the semantics of data wrangling ...
05/23/2018

Predicting football tables by a maximally parsimonious model

This paper presents some useful mathematical results involved in footbal...
10/04/2022

Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning

Dataset discovery from data lakes is essential in many real application ...