The importance of manual assessment in link discovery

Authors:
Wei Che Huang;Andrew Trotman;Shlomo Geva
Affiliations:
Queensland University of Technology, Brisbane, Australia;University of Otago, Dunedin, New Zealand;Queensland University of Technology, Brisbane, Australia
Venue:
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Year:
2009

Citing 2
Cited 2

Wikify!: linking documents to encyclopedic knowledge

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to link with wikipedia

Proceedings of the 17th ACM conference on Information and knowledge management

Generating links to background knowledge: a case study using narrative radiology reports

Proceedings of the 20th ACM international conference on Information and knowledge management
An evaluation framework for cross-lingual link discovery

Information Processing and Management: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Using a ground truth extracted from the Wikipedia, and a ground truth created through manual assessment, we show that the apparent performance advantage seen in machine learning approaches to link discovery are an artifact of trivial links that are actively rejected by manual assessors.