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

About

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.

Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton · 2018

Related benchmarks

Task	Dataset	Result
Column Type Annotation	T2D Cross-Domain	F1 Score94.7	14
Column Type Annotation	Efthymiou Cross-Domain	F1 Score61.9	14
Column Type Annotation	Limaye Cross-Domain	F1 Score59.7	14
Column Type Prediction	Limaye PK Columns v1 (test)	Precision0.796	10
Column Type Prediction	T2D All Columns v2	Precision91.7	8
Column Type Prediction	T2D PK Columns v2	Precision96.7	8
Semantic Column Type Prediction	Limaye	Accuracy91.2	7
Semantic Column Type Prediction	Efthymiou	Accuracy82.7	7
Semantic Column Type Prediction	T2D (test)	Accuracy94.7	7