[Corpora-List] Reminder CFP: Journal of Logic and Computation - Special Issue on Natural Language and Knowledge Representation

From: jana sukkarieh (jana.sukkarieh@secerno.com)
Date: Fri Jul 14 2006 - 15:48:35 MET DST

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    CALL FOR PAPERS

    We cordially invite submissions of articles for a special issue of the journal of logic and computation <http://logcom.oxfordjournals.org/> on natural language and knowledge representation.

    Submission deadline: July 31st, 2006.

    TOPICS

    We believe that the Natural Language Processing (NLP) and the Knowledge Representation (KR) communities have common goals. They are both concerned with representing knowledge and with reasoning, since the best test for the semantic capability of an NLP system is performing reasoning tasks. Having these two essential common grounds, the two communities ought to have been collaborating, to provide a well-suited representation language that covers these grounds. However, the two communities also have difficult-to-meet concerns.
    Mainly, the semantic representation (SR) should be expressive enough and take the information in context into account, while the KR should be equipped with a fast reasoning process.

    The main objection against using an SR or a KR is that they need experts to be understood. Non-experts communicate (usually) via a natural language (NL), and more or less they understand each other while performing a lot of reasoning. An essential practical value of representations is their attempt to be transparent. This will particularly be useful when/if the system provides a justification for a user or a knowledge engineer on its line of reasoning using the underlying KR (i.e. without generating back to NL).

    We all seem to believe that, compared to Natural Language, the existing Knowledge Representation and reasoning systems are poor. Nevertheless, for a long time, the KR community has dismissed the idea that NL can be a KR. That's because NL can be very ambiguous and there are syntactic and semantic processing complexities associated with it. However, researchers in both communities have started looking at this issue again. Possibly, it has to do with the NLP community making some progress in terms of processing and handling ambiguity, the KR community realising that a lot of knowledge is already 'coded' in NL and that one should reconsider the way they handle expressivity and ambiguity.

    For this special journal issue of logic and computation, we invite the
    submission of original high quality articles. Topics for this special
    issue include but not limited to:

    + A novel NL-like KR or building on an existing one

    + Reasoning systems that benefit from properties of NL to reason with NL

    + Semantic representation used as a KR : compromise between expressivity and efficiency?

    + More Expressive KR for NL understanding (Any compromise?)

    + Any work exploring how existing representations fall short of addressing some problems involved in modelling, manipulating or reasoning (whether reasoning as used to get an interpretation for a certain utterance, exchange of utterances or what utterances follow
    from other utterances) with NL documents

    + Representations that show how classical logics are not as efficient,
    transparent, expressive or where a one-step application of an inference rule require more (complex) steps in a classical environment and vice-versa; i.e.
    how classical logics are more powerful, etc.

    + Building a reasoning test collection for natural language understanding
    systems: any kind of reasoning (deductive, abductive, etc); for a deductive test suite see for e.g. deliverable 16 of the FraCas project. Also, look at textual entailment challenges 1 and 2.

    + Comparative results (on a common test suite or a common task) of different representations or systems that reason with NL (again any kind of reasoning). The comparison could be either for efficiency, transparency or expressivity

    + Knowledge acquisition systems or techniques that benefit from properties of NL to acquire knowledge already "coded" in NL

    + Automated Reasoning, Theorem Proving and KR communities views on all this

    + Challenges in Natural Language and Reasoning

    + Where is the NLP or KR community going wrong/right in meeting the
    challenges?

    PROGRAM COMMITTEE

    James ALLEN, University of Rochester, USA

    Patrick BLACKBURN, Institut National de Recherche en Informatique, France

    Johan BOS, Università di Roma "La Sapienza", Italy

    Alan BUNDY, University of Edinburgh, UK

    Harry BUNT, Tilburg University, The Netherlands

    Richard CROUCH, Palo Alto Research Centre, USA

    Ido DAGAN, Bar Ilan University, Israel

    Claire GARDENT, Centre national de la recherche scientifique, France

    Fernando GOMEZ, University of Central Florida, USA

    Sanda HARABAGIU, University of Texas at Dallas, USA

    John HARRISON, Intel Corporation, USA

    Jerry HOBBS, Information Sciences Institute, USA

    Chung Hee HWANG, Raytheon Co., USA Ewan KLEIN, University of Edinburgh, UK

    Michael KOHLHASE, International University Bremen, Germany

    Shalom LAPPIN, King's College, UK

    Carsten LUTZ, Dresden University of Technology, Germany

    Inderjeet MANI, George Town University, USA

    David MCALLESTER, Toyota Technological Institute at Chicago, USA

    Jeff PELLETIER, Simon Fraser University, Canada

    Stephen PULMAN, University of Oxford, UK

    Allan RAMSAY, The University of Manchester, UK

    Lenhart SCHUBERT, University of Rochester, USA

    John SOWA, VivoMind Intelligence, Inc., USA

    Jana SUKKARIEH, Secerno Ltd, UK

    Geoff SUTCLIFFE, Miami University, USA

    Jan VAN EIJCK, Utrecht University & CWI, The Netherlands

    Paper submission deadline is July 31st, 2006. Submission process is available on http://users.ox.ac.uk/~lady0641/Flairs06_NL_KR/journal_issue.html
    and the online submission system is available on :
    <http://www.easychair.org/NLKR2006/>

    The articles will be peer reviewed and notification for authors will be sent as soon as possible after the date of submission.

    For any queries please contact <jana.sukkarieh@cantab.net>



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