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

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https://aaai.org/ojs/index.php/AAAI/article/view/3765
Original languageEnglish
Title of host publicationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Subtitle of host publicationThirty-First Conference on Innovative Applications of Artificial Intelligence The Ninth Symposium on Educational Advances in Artificial Intelligence - AAAI Technical Track: AI and the Web
Place of PublicationHonolulu, Hawaii, United States
PublisherAAAI Press
Pages29-36
Number of pages8
Volume33
ISBN (Electronic)978-1-57735-809-1
DOIs
Publication statusPublished - 23 Jul 2019
EventThe Thirty-Third AAAI Conference on Artificial Intelligence - Hilton Hawaiian Village, Honolulu, Hawaii, United States
Duration: 27 Jan 20191 Feb 2019
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceThe Thirty-Third AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2019
CountryUnited States
CityHonolulu, Hawaii
Period27/01/191/02/19
Internet address

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.

Event

The Thirty-Third AAAI Conference on Artificial Intelligence

27/01/191/02/19

Honolulu, Hawaii, United States

Event: Conference

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