CfP AIJ Special Issue on AI, Wikipedia and Semi-Structured Resources


The availability of large amounts of wide-coverage semantic knowledge, 
and the ability to extract it using the powerful new statistical machine 
learning techniques developed and used in various branches of AI, is 
making possible significant advances in applications that require deep 
understanding capabilities such as question-answering engines and dialogue 
systems. Though well-known problems such as high cost and scalability 
discouraged the development of knowledge-rich approaches in the past, more 
recently the increasing availability of online collaborative resources 
has attracted the attention of much work in the AI community. 
Collaboratively constructed knowledge repositories have in fact been 
used as wide-coverage sources of semi-structured information and manual 
annotations. When coupled with free-form natural language information, 
these resources enable the development of large-scale structured resources 
using knowledge-lean applications. Wikipedia is a case in point, being the 
largest and most popular collaborative and multilingual resource of world 
and linguistic knowledge that contains unstructured and (semi-)structured 

This special issue aims to collect state-of-the-art contributions to the 
development and use of hybrid (structured, semi-structured, and 
unstructured) resources in AI. These include, but are not limited to, 
semi-structured encyclopedic resources such as Wikipedia (and related 
projects such as Wiktionary), user-generated answer repositories such as 
Wiki and Yahoo! Answers, and collaborative tagging efforts on social media 
platforms such as Flickr and Blogger. Hybrid knowledge resources such as 
Wikipedia enable the development of methods for extracting, bootstrapping 
and integrating fully structured, machine-readable knowledge from both 
unstructured and semi-structured origins. Such induced wide-coverage 
knowledge is expected to prove beneficial for a variety of AI tasks, as well 
as the Semantic Web. We are particularly interested in articles showing the 
benefits of using such resources and AI techniques synergistically. We thus 
welcome contributions dealing with applications of general AI methodologies 
for the construction and validation of large-scale machine-readable 
knowledge repositories and the impact of automatically-extracted knowledge 
for AI applications. We also encourage contributors to investigate the 
nature and impact of the structured and unstructured parts of the resource 
(e.g. information redundancy, overlaps, connections, etc.). 


Topics of interest include, but are not limited to:

 * Using Wikipedia and other semi-structured content in AI tasks. 
   Examples include Word Sense Disambiguation, Information Retrieval, 
   Information Extraction, Question Answering, etc.
 * Automatic transformation of hybrid knowledge repositories into 
   fully-structured resources
 * Extraction and formalization of information from hybrid resources 
   into knowledge bases and databases
 * Automatic integration of semi-structured knowledge repositories with 
   structured resources (e.g. Cyc, WordNet, SUMO)
 * Enriching encyclopedic and semi-structured entries with new types of 
   structural information
 * Wikipedia and the Semantic Web
 * Automatic extraction and use of cross-lingual information, and other 
   multilingual aspects of Wikipedias and Wiktionaries in AI
 * Knowledge acquisition from collaborative user contributions
 * AI methods for improving the quality of (semi-)structured 
   user contributions


Deadline for submissions: October 31, 2010. Please follow the submission 
instructions available from the AIJ webpage under Submit Article

For additional information, please contact Simone Paolo Ponzetto 


 * Submission deadline: October 31, 2010
 * First-round reviews due: January 31, 2011
 * Revised versions due: May 30, 2011
 * Second-round reviews due: June 30, 2011
 * Final versions due: July 31, 2011
 * Special issue publication: Fall 2011

Received on Monday, 8 March 2010 17:51:56 UTC