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

TL;DR

ColNet is a neural network framework that enhances web table column type prediction by integrating knowledge base reasoning with contextual and locality semantics, outperforming existing methods.

Contribution

It introduces a novel neural network approach that combines KB reasoning, lookup, and local semantics for improved column type annotation.

Findings

Achieves higher accuracy than state-of-the-art methods on multiple datasets.

Effectively integrates KB reasoning with contextual and locality features.

Demonstrates robustness on web and Wikipedia table datasets.

Abstract

Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity correspondences of cells in the KB, and may fail to deal with growing web tables with incomplete meta information. In this paper we propose a neural network based column type annotation framework named ColNet which is able to integrate KB reasoning and lookup with machine learning and can automatically train Convolutional Neural Networks for prediction. The prediction model not only considers the contextual semantics within a cell using word representation, but also embeds the semantics of a column by learning locality features from multiple cells. The method is evaluated with DBPedia and two different web table datasets, T2Dv2 from the general Web and Limaye from Wikipedia pages, and achieves higher performance than the state-of-the-art approaches.