Artificial Intelligence Journal Special Issue on "AI, Wikipedia and Semi-Structured Resources" now online!

Dear colleagues,

we are delighted to announce the publication of the Artificial
Intelligence Journal Special Issue on "Artificial Intelligence,
Wikipedia and Semi-Structured Resources". This Special Issue aims at
providing a comprehensive picture of the state of the art of research
that exploits semi-structured resources (Wikipedia, most notably) for
Artificial Intelligence (AI) and Natural Language Processing (NLP). The
issue is the result of a highly selective publication process which
involved more than 70 submissions and 100 reviewers: as a result, it
presents a series of top-research contributions, which together provide
a comprehensive and up-to-date overview of the many different lines of
research that have been, and are being, pursued in this highly active
area of investigation.

All papers can be accessed from the Artificial Intelligence Journal
web-page (see under either "View Articles" or "Special Issues")

  http://www.journals.elsevier.com/artificial-intelligence

or accessed directly via Elsevier's ScienceDirect system

  http://www.sciencedirect.com/science/journal/00043702/194

Please note that the AI Journal can be accessed free of charge following
the instructions found here:

  http://www.ida.liu.se/ext/aijd/top/free-access/page.html

We wish you to enjoy the papers and hope that you will find them useful
in your own research!

The AI Journal Special Issue Guest Editors:
  Eduard Hovy, Roberto Navigli and Simone Paolo Ponzetto

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Artificial Intelligence, Volume 194, Pages 1-252 (January 2013)
TABLE OF CONTENTS

Eduard Hovy, Roberto Navigli, Simone Paolo Ponzetto: Editorial (p. 1).

- Introduction

Eduard Hovy, Roberto Navigli, Simone Paolo Ponzetto: Collaboratively
built semi-structured content and Artificial Intelligence: The story so
far (pp. 2-27).

- Acquiring knowledge

Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, Gerhard Weikum:
YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
(pp. 28-61).

Vivi Nastase, Michael Strube: Transforming Wikipedia into a large scale
multilingual concept network (pp. 62-85).

- IR applications

Pekka Malo, Pyry Siitari, Ankur Sinha: Automated query learning with
Wikipedia and genetic programming (pp. 86-110).

Rianne Kaptein, Jaap Kamps: Exploiting the category structure of
Wikipedia for entity ranking (pp. 111-129).

- NLP applications

Ben Hachey, Will Radford, Joel Nothman, Matthew Honnibal, James
R. Curran: Evaluating Entity Linking with Wikipedia (pp. 130-150).

Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James
R. Curran: Learning multilingual named entity recognition from Wikipedia
(pp. 151-175).

Majid Yazdani, Andrei Popescu-Belis: Computing text semantic relatedness
using the contents and links of a hypertext encyclopedia (pp. 176-202).

Sara Tonelli, Claudio Giuliano, Kateryna Tymoshenko: Wikipedia-based WSD
for multilingual frame annotation (pp. 203-221).

- Tools

David Milne, Ian H. Witten: An open-source toolkit for mining Wikipedia
(pp. 222-239).

- Neuroimaging

Francisco Pereira, Matthew Botvinick, Greg Detre: Using Wikipedia to
learn semantic feature representations of concrete concepts in
neuroimaging experiments (pp. 240-252).

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