WikiAI 2010 — Call for Participation AAAI-2010 Workshop on Collaboratively-built Knowledge Sources and Artificial Intelligence

WikiAI 2010 — Call for Participation
AAAI-2010 Workshop on Collaboratively-built Knowledge Sources and Artificial Intelligence
Atlanta, Georgia USA, July 11th 2010, in conjunction with AAAI 2010

The goal of this workshop is to foster the research and dissemination of ideas on the mutually beneficial interaction between repositories of user-contributed knowledge and AI.
Until recently, the AI and in particular the NLP community have relied on resources built manually by experts in specific areas (in particular linguists, philosophers, cognitive linguists). User contributed knowledge has opened up a new perspective, in that it captures the kind of knowledge and organization that arises naturally out of the consensus of the masses, and as such represents better our collective knowledge. The outcome is a multi-faceted and extremely rich source of information, revealed through embedded annotations and structural information.
The first such collaboratively developed repository of information to be extensively used in AI and NLP was Wikipedia. Its usefulness was demonstrated through its contributions to a wide range of tasks: text categorization, clustering, word sense disambiguation, information retrieval, information extraction, question answering. The following bibliographies include relevant papers published at established conferences in the area of NLP and AI (ACL, EACL, NAACL, EMNLP, AAAI, SIGIR, IUI and other venues):
Wikipedia in Academic Studies
Wikipedia as an Academic Source
Wiki Research Bibliography
In recent years, more and more resources and collaborative endeavours have started to be incorporated and exploited as knowledge repositories for various tasks. Tags associated with images in Flickr, question-answer collections in Yahoo! Answers are a few examples of such information sources. Amazon’s Mechanical Turk gives researchers access to “human computation” power, and is being used more and more as a solution to the difficult problems of large scale evaluations and data annotation, both crucial for the continuous development of the AI and NLP fields.
The workshop is a successor to the ones organized at AAAI 2008 entitled “Wikipedia and Artificial Intelligence: An Evolving Synergy” (WikiAI 08) and at IJCAI 2009 entitled “User contributed knowledge and Artificial Intelligence: An Evolving Synergy” (WikiAI09). We follow the trend started in 2009 of keeping the scope of the workshop broad, to include a variety of user-contributed sources of knowledge: Wikipedia, Wiktionary, Wiki Answers, Yahoo! Answers, Flickr, Freebase, Amazon’s Mechanical Turk, blogs, and more.


9:00 – 9:15 Opening remarks
9:15 – 10:15 Invited talk: Treating Expert Knowledge as Common Sense
Henry Lieberman
10:15 – 10:45 Coffee break
10:45 – 11:15 Mixed-Initiative, Entity-Centric Data Aggregation using Assistopedia
Matthew Michelson, Sofus Macskassy, Steve Minton
11:15 – 11:45 Constructing Folksonomies by Integrating Structured Metadata with Relational Clustering
Anon Plangprasopchok, Kristina Lerman, Lise Getoor
11:45 – 12:15 Bridging Common Sense Knowledge Bases with Analogy by Graph Similarity
Yen-ling Kuo, Jane Yung-jen Hsu
12:15 – 13:30 Lunch break
13:30 – 14:30 Invited talk: Can We (and Should We) Make Formal Sense of General Knowledge Expressed in Ordinary Language?
Lenhart Schubert
14:30 – 15:00 Learning to Extract Quality Discourse in Online Communities
Michael Brennan, Stacey Wrazien, Rachel Greenstadt
15:00 – 15:15 Open Mind Common Sense: Crowd-sourcing for Common Sense
Catherine Havasi, Robert Speer, Kenneth Arnold, Henry Lieberman, Jason Alonso, Jesse Moeller
15:15 – 15:45 Coffee break and demo
15:45 – 16:15 Learning from the Web: Extracting General World Knowledge from Noisy Text
Jonathan Gordon, Benjamin Van Durme, Lenhart Schubert
16:15 – 16:45 Reducing the Dimensionality of Data Streams Using Common Sense
Catherine Havasi, Jason Alonso, Robert Speer
16:45 – 17:15 Approaches for Enriching Wikipedia
Zareen Syed, Tim Finin
17:15 – 17:35 Aligning WordNet Synsets and Wikipedia Articles
Samuel Fernando, Mark Stevenson
17:35 – 18:00 Free discussions and closing remarks

Call For Papers

AI and NLP have the potential to both exploit and dig deeper in the mines of collective knowledge, and to help build them, by providing tools for helping generate more, better and consistent content. As with the previous events, we believe work in this area should be encouraged, followed and popularized, to promote the synergy between repositories of user-contributed knowledge and research in Artificial Intelligence.
The workshop is intended to be highly interdisciplinary. We encourage participation of researchers from different perspectives, including (but not limited to) machine learning, computational linguistics, information retrieval, information extraction, question answering, knowledge representation, human computer interaction and others. We also encourage participation of researchers from other areas who might benefit from the use of large bodies of machine-readable knowledge.
An upcoming special issue of the Artificial Intelligence Journal will be devoted to the topic of Artificial Intelligence, Wikipedia, and Semi-Structured Resources.
Topics covered by this workshop include, but are not limited to:
Using user-contributed knowledge as a source of training data for AI tasks (both supervised and unsupervised)
Automatic methods for improving the quality of user contributions
Modeling tasks for human computation
Integrating different resources (e.g. Wikipedia and WN/Cyc/other ontologies)
Extracting annotated data from user contributions
Enriching user contributions with new types of structural information
User-contributed knowledge and the Semantic Web/Web 2.0
Automatic extraction and use of cross-lingual information
Computerized use of satellite Wiki projects such as Wiktionary, Wikibooks or Wikispecies
Human computation like Amazon Mechanical Turk to help AI tasks
Data mining on collaboratively-contributed resources
Innovative graph algorithms exploiting collaborative resources
Word Sense Disambiguation with Wikipedia, Wiktionary, etc.

For additional information:

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