3 Scopus citations


Wikipedia has become one of the best sources for creating and sharing a massive volume of human knowledge. Much effort has been devoted to generating and enriching the structured data by automatic information extraction from unstructured text in Wikipedia. Most, if not all, of the existing work share the same paradigm, that is, starting with information extraction over the unstructured text data, followed by supervised machine learning. Although remarkable progresses have been made, this paradigm has its own limitations in terms of effectiveness, scalability as well as the high labeling cost. We present WiiCluster, a scalable platform for automatically generating infobox for articles in Wikipedia. The heart of our system is an effective cluster-then-label algorithm over a rich set of semi-structured data in Wikipedia articles: linked entities. It is totally unsupervised and thus does not require any human label. It is effective in generating semantically meaningful summarization for Wikipedia articles. We further propose a cluster-reuse algorithm to scale up our system. Overall, our WiiCluster is able to generate nearly 10 million new facts. We also develop a web-based platform to demonstrate WiiCluster, which enables the users to access and browse the generated knowledge.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Number of pages3
ISBN (Print)9781450325981
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014


Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014


  • Cluster visualization
  • Knowledge extraction
  • Summarization

ASJC Scopus subject areas

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

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