Autonomously semantifying wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Automatically refining the wikipedia infobox ontology
Proceedings of the 17th international conference on World Wide Web
Freebase: a collaboratively created graph database for structuring human knowledge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Using Links to Classify Wikipedia Pages
Advances in Focused Retrieval
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Classifying Wikipedia articles into NE's using SVM's with threshold adjustment
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Classifying Wikipedia entities into fine-grained classes
ICDEW '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops
Automatic Mapping of Wikipedia Templates for Fast Deployment of Localised DBpedia Datasets
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
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Given the sheer amount of work and expertise required in authoring Wikipedia articles, automatic tools that help Wikipedia contributors in generating and improving content are valuable. This paper presents our initial step towards building a full-fledged author assistant, particularly for suggesting infobox templates for articles. We build SVM classifiers to suggest infobox template types, among a large number of possible types, to Wikipedia articles without infoboxes. Different from prior works on Wikipedia article classification which deal with only a few label classes for named entity recognition, the much larger 337-class setup in our study is geared towards realistic deployment of infobox suggestion tool. We also emphasize testing on articles without infoboxes, due to that labeled and unlabeled data exhibit different distributions of features, which departs from the typical assumption that they are drawn from the same underlying population.