Proceedings of the WSDM Cup 2017: Vandalism Detection and Triple Scoring

The WSDM Cup 2017 was a data mining challenge held in conjunction with the 10th International Conference on Web Search and Data Mining (WSDM). It addressed key challenges of knowledge bases today: quality assurance and entity search. For quality assurance, we tackle the task of vandalism detection,...

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Bibliographic Details
Published in:arXiv.org 2017-12
Main Authors: Potthast, Martin, Heindorf, Stefan, Bast, Hannah
Format: Article
Language:English
Subjects:
Cloud computing
Data mining
Data search
Knowledge bases (artificial intelligence)
Quality assurance
Quality control
Reproducibility
Vandalism
Online Access:Get full text
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Summary:The WSDM Cup 2017 was a data mining challenge held in conjunction with the 10th International Conference on Web Search and Data Mining (WSDM). It addressed key challenges of knowledge bases today: quality assurance and entity search. For quality assurance, we tackle the task of vandalism detection, based on a dataset of more than 82 million user-contributed revisions of the Wikidata knowledge base, all of which annotated with regard to whether or not they are vandalism. For entity search, we tackle the task of triple scoring, using a dataset that comprises relevance scores for triples from type-like relations including occupation and country of citizenship, based on about 10,000 human relevance judgements. For reproducibility sake, participants were asked to submit their software on TIRA, a cloud-based evaluation platform, and they were incentivized to share their approaches open source.
ISSN:2331-8422