AutonomouslySemantifyingWikipedia.PDFVIP

Autonomously Semantifying Wikipedia Fei Wu Daniel S. Weld Computer Science Engineering Department, University of Washington, Seattle, WA, USA {wufei,weld}@ ABSTRACT Berners-Lee’s compelling vision of a Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method — creating enough structured data to motivate the development of applications. This paper argues that autonomously “Semantifying Wikipedia” is the best way to solve the problem. We choose Wikipedia as an initial data source, because it is comprehensive, not too large, high-quality, and contains enough manuallyderived structure to bootstrap an autonomous, self-supervised process. We identify several types of structures which can be automatically enhanced in Wikipedia (e.g., link structure, taxonomic data, infoboxes, etc.), and we describe a prototype implementation of a self-supervised, machine learning system which realizes our vision. Preliminary experiments demonstrate the high precision of our system’s extracted data — in one case equaling that of humans. Categories and Subject Descriptors H.4 [Information Systems Applications]: Miscellaneous General Terms Management, Design Keywords Information Extraction, Wikipedia, Semantic Web 1. INTRODUCTION While compelling in the long term, Berners-Lee’s vision of the Semantic Web [5] is developing slowly. Researchers have argued that the relative dif?culty of authoring structured data is a primary cause [17]. A chicken-and-egg problem results: if there was more structured data available, people would develop applications; but without compelling applications, it is not worth people’s time to structure their data. In order to break this deadlock, a bootstrapping method is needed — some method of automatically structuring a large amount of existing data. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distribute