
    UMLS::Similarity

  SYNOPSIS
    This package consists of Perl modules along with supporting Perl
    programs that implement the semantic relatedness measures described by
    Leacock & Chodorow (1998) and a simple path based measure. In the near
    future, we are planning to add Jiang & Conrath (1997), Resnik (1995) and
    Lin (1998).

    This package is essentially a copy of Semantic::Similarity which is a
    re-implementation of the WordNet::Similarity suite of modules.
    WordNet::Similarity is tied to the WordNet lexical database. But,
    suppose we wish to use these techniques in the domain of medical
    informatics, for instance. This Semantic::Similarity allows one to
    replace WordNet with another domain-specific taxonomy, and use this to
    find semantic relatedness of concepts in that domain.

    Semantic::Similarity is not tied to a specific database but requires an
    Interface module (such as SnoMed::Interface) communicate between it and
    the database. Currently, we created UMLS::Interface to connect with the
    UMLS to be used with this module. In the future, we plan that
    UMLS-Interface will work seemlessly with all of the Semantic::Similarity
    functionality not just what is available in UMLS::Similarity.

    The Perl modules are designed as objects with methods that take as input
    two word senses. The semantic relatedness of these word senses is
    returned by these methods. A quantitative measure of the degree to which
    two word senses are related has wide ranging applications in numerous
    areas, such as word sense disambiguation, information retrieval, etc.
    For example, in order to determine which sense of a given word is being
    used in a particular context, the sense having the highest relatedness
    with its context word senses is most likely to be the sense being used.
    Similarly, in information retrieval, retrieving documents containing
    highly related concepts are more likely to have higher precision and
    recall values.

    The following sections describe the organization of this software
    package and how to use it. A few typical examples are given to help
    clearly understand the usage of the modules and the supporting
    utilities.

  SEMANTIC RELATEDNESS
        We observe that humans find it extremely easy to say if two words are
        related and if one word is more related to a given word than another.
        For example, if we come across two words -- 'car' and 'bicycle', we know
        they are related as both are means of transport. Also, we easily observe
        that 'bicycle' is more related to 'car' than 'fork' is. But is there
        some way to assign a quantitative value to this relatedness? Some ideas
        have been put forth by researchers to quantify the concept of
        relatedness of words, with encouraging results.

        A number of different measures of relatedness have been implemented in
        this software package. These include a simple edge counting
        approach. The measures require a backend taxonomy that defines concepts
        in a domain (or in general), and some basic relationships between these
        concepts.

  CONTENTS
        All the modules that will be installed in the Perl system directory are
        present in the '/lib' directory tree of the package. These include the
        semantic relatedness modules -- 

          Semantic/Similarity/jcn.pm
          Semantic/Similarity/path.pm

        -- present in the lib/ subdirectory. All these modules, once installed
        in the Perl system directory, can be directly used by Perl programs.

        The package contains a utils/ directory that contain Perl utility 
        programs. These utilities use the modules or provide some supporting
        functionality.

          queryUMLS.pl -- returns the semantic similarity of two 
                          terms or UMLS CUIs given a specified 
                          measure
      
  INSTALL
        To install these modules run the following magic commands:

          perl Makefile.PL
          make
          make test
          make install

        This will install the modules in the standard locations. You will, most
        probably, require root privileges to install in standard system
        directories. To install in a non-standard directory, specify a prefix
        during the 'perl Makefile.PL' stage as:

          perl Makefile.PL PREFIX=/home/sid

        It is possible to modify other parameters during installation. The
        details of these can be found in the ExtUtils::MakeMaker
        documentation. However, it is highly recommended not messing around
        with other parameters, unless you know what you're doing.

  TAXONOMY INTERFACE
        The modules implemented in this package require a backend taxonomy for
        computing semantic relatedness. A taxonomy is provided to these modules
        as an interface object. An interface object (for example
        Snomed::Interface v0.01) is a Perl module that provides certain methods
        that can be used by the Semantic::Similarity modules to access the
        taxonomy. The following methods are expected in the interface object:

          $version = $interface->version();
          $depth   = $interface->depth();
          $bool    = $interface->exists($concept);
          @tList   = $interface->getTermList($concept);
          @cList   = $interface->getConceptList($term);
          @path    = $interface->findShortestPath($concept1, $concept2);
    
        The 'version' method returns the version of the UMLS that is 
        being used. The 'depth' method returns the max depth of the 
        view of the UMLS that is being used. The 'exists' method checks 
        if a concept exists in the view of the UMLS being used. The 
        'getTermsList' method lists all terms corresponding to a 
        concept in the given UMLS viewand the 'getConceptList' method
        retrieves the list of CUIs corresponding to a given term. The 
        'findShortestPath' method returns the shortest path between 
        two CUIs given the view of the UMLS being used.

        Right now we know that this package works with UMLS::Interface.
    
  SOFTWARE COPYRIGHT AND LICENSE
        Copyright (C) 2004-2009 Bridget T McInnes,  Siddharth Patwardhan, 
        Serguei Pakhomov and Ted Pedersen

        This suite of programs is free software; you can redistribute it and/or
        modify it under the terms of the GNU General Public License as published
        by the Free Software Foundation; either version 2 of the License, or (at
        your option) any later version.

        This program is distributed in the hope that it will be useful, but
        WITHOUT ANY WARRANTY; without even the implied warranty of
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
        General Public License for more details.

        You should have received a copy of the GNU General Public License along
        with this program; if not, write to the Free Software Foundation, Inc.,
        59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.

        Note: The text of the GNU General Public License is provided in the file
        'GPL.txt' that you should have received with this distribution.

  ACKNOWLEDGMENTS
        We would like to thank the following for their support and contribution
        towards the development of this package. We thank Jason Rennie for his
        QueryData package, the WordNet guys at Princeton for WordNet, Resnik,
        Hirst, St-Onge, Jiang, Conrath, Lin, Wu, Palmer, Leacock, and Chodorow
        for their algorithms and work on the relatedness measures. We also thank
        Bano (Satanjeev Banerjee) for his work on the adapted gloss overlap
        module.

  REFERENCES
        1   Wu Z. and Palmer M. 1994. Verb Semantics and Lexical Selection. In
            Proceedings of the 32nd Annual Meeting of the Association for
            Computational Linguistics.  Las Cruces, New Mexico.

        2   Resnik P. 1995. Using information content to evaluate semantic
            similarity. In Proceedings of the 14th International Joint
            Conference on Artificial Intelligence, pages 448-453, Montreal.

        3   Jiang J. and Conrath D. 1997. Semantic similarity based on corpus
            statistics and lexical taxonomy. In Proceedings of International
            Conference on Research in Computational Linguistics, Taiwan.

        4   Fellbaum C., editor. WordNet: An electronic lexical database. MIT
            Press, 1998.

        5   Leacock C. and Chodorow M. 1998. Combining local context and WordNet
            similarity for word sense identification. In Fellbaum 1998, pp.
            265-283.

        6   Lin D. 1998. An information-theoretic definition of similarity. In
            Proceedings of the 15th International Conference on Machine
            Learning, Madison, WI.

        7   Hirst G. and St-Onge D. 1998. Lexical Chains as representations of
            context for the detection and correction of malapropisms. In
            Fellbaum 1998, pp. 305-332.

        8   Schtze H. 1998. Automatic Word Sense Discrimination. Computational
            Linguistics, 24(1):97-123.

        9   Resnik P. 1999. Semantic Similarity in a Taxonomy: An Information-
            Based Measure and its Applications to Problems of Ambiguity in
            Natural Language. Journal of Artificial Intelligence Research, 11,
            95-130.

        10  Budanitsky A. and Hirst G. 2001. Semantic distance in WordNet: An
            experimental, application-oriented evaluation of five measures. In
            Workshop on WordNet and Other Lexical Resources, Second meeting of
            the North American Chapter of the Association for Computational
            Linguistics. Pittsburgh, PA.

        11  Banerjee S. and Pedersen T. 2002. An Adapted Lesk Algorithm for Word
            Sense Disambiguation Using WordNet. In Proceeding of the Fourth
            International Conference on Computational Linguistics and
            Intelligent Text Processing (CICLING-02). Mexico City.

        12  Patwardhan S., Banerjee S. and Pedersen T. 2002. Using Semantic
            Relatedness for Word Sense Disambiguation. In Proceedings of the
            Fourth International Conference on Intelligent Text Processing and
            Computational Linguistics, Mexico City.

        13  Banerjee S. Adapting the Lesk algorithm for word sense
            disambiguation to WordNet. Master Thesis, University of Minnesota,
            Duluth, 2002.

        14  Patwardhan S. Incorporating dictionary and corpus information into a
            vector measure of semantic relatedness. Master Thesis, University of
            Minnesota, Duluth, 2003.

    SEE ALSO

        <http://groups.yahoo.com/group/wn-similarity>,
        <http://search.cpan.org/dist/WordNet-Similarity>,
        <http://wn-similarity.sourceforge.net>

    AUTHORS

         Bridget T McInnes, University of Minnesota Twin Cities
         bthomson at cs.umn.edu

         Siddharth Patwardhan, University of Utah
         sidd at cs.utah.edu

         Serguei Pakhomov, University of Minnesota Twin Cities
         pakh002 at umn.edu

         Ted Pedersen, University of Minnesota Duluth
         tpederse at d.umn.edu

    DOCUMENTATION COPYRIGHT AND LICENSE

        Copyright (C) 2003-2009 Bridget T. McInnes, Siddharth Patwardhan, 
        Serguei Pakhomov and Ted Pedersen.

        Permission is granted to copy, distribute and/or modify this document
        under the terms of the GNU Free Documentation License, Version 1.2 or
        any later version published by the Free Software Foundation; with no
        Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.

        Note: a copy of the GNU Free Documentation License is available on the
        web at <http://www.gnu.org/copyleft/fdl.html> and is included in this
        distribution as FDL.txt.

