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NEW !!! List of Accepted_Papers

[edit] Workshop overview

Since its inception less than seven years ago, Wikipedia has become one of the largest and fastest growing online sources of encyclopedic knowledge. One of the reasons why Wikipedia is appealing to contributors and users alike is the richness of its embedded structural information: articles are hyperlinked to each other and connected to categories from an ever expanding taxonomy; pervasive language phenomena such as synonymy and polysemy are addressed through redirection and disambiguation pages; entities of the same type are described in a consistent format using infoboxes; related articles are grouped together in series templates.


As a large-scale repository of structured knowledge, Wikipedia has become a valuable resource for a diverse set of Artificial Intelligence (AI) applications. Major conferences in natural language processing and machine learning have recently witnessed a significant number of approaches that use Wikipedia for tasks ranging from text categorization and clustering to word sense disambiguation, information retrieval, information extraction and question answering. On the other hand, Wikipedia can greatly benefit from numerous algorithms and representation models developed during decades of AI research, as illustrated recently in tasks such as estimating the reliability of authors' contributions, automatic linking of articles, or intelligent matching of Wikipedia tasks with potential contributors.


The goal of the workshop is to foster the research and dissemination of ideas on the mutually beneficial interaction between Wikipedia and AI. The workshop is intended to be highly interdisciplinary. We encourage participation of researchers working on Wikipedia from different perspectives, including (but not limited to) machine learning, computational linguistics, information retrieval, information extraction, question answering, knowledge representation, and others. We also encourage participation of researchers from other areas who might benefit from the use of a large body of machine-readable knowledge.


[edit] What's New?

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