UMLS::Similarity             
=head2 SYNOPSIS
      This package consists of Perl modules along with supporting Perl
      programs that implement the semantic similarity and relatedness 
      measures described by Leacock & Chodorow (1998), Wu & Palmer (1994), 
      Nguyen and Al-Mubaid  (2006), Rada, et. al. 1989, Jiang & Conrath 
      (1997), Resnik (1995), Lin (1998), Banerjee and Pedersen(2002), 
      Patwardhan and Pedersen (2006) and a simple path  based measure.

      UMLS::Similarity requires the UMLS::Interface module to access 
      the Unified Medical Language System (UMLS) in order to determine 
      the similarity between two UMLS concepts.

      The Perl modules are designed as objects with methods that take as
      input two concepts from the UMLS. The semantic relatedness of these 
      concepts is returned by these methods. A quantitative measure of 
      the degree to which the two concepts 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 the UMLS-Interface that define UMLS 
        concepts, 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 -- 

          UMLS/Similarity/lch.pm
          UMLS/Similarity/path.pm
          UMLS/Similarity/wup.pm
          UMLS/Similarity/nam.pm
          UMLS/Similarity/cdist.pm
          UMLS/Similarity/res.pm
          UMLS/Similarity/lin.pm
          UMLS/Similarity/jcn.pm
          UMLS/Similarity/random.pm
          UMLS/Similarity/vector.pm (beta)
          UMLS/Similarity/lesk.pm (beta)

        -- 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.

          umls-similarity.pl         -- returns the semantic similarity of two 
                                        terms or UMLS CUIs given a specified 
                                        measure (and view of the UMLS).

          spearman.pl                -- calculates the Spearman Rank 
                                        Correlation between two files

          vector-input.pl            -- creates the matrix and index files 
                                        required for the vector measure

          SignificanceTesting.r      -- R script to calculate the correlation 
                                        between a gold standard and the results 
                                        obtained using the measures in the 
                                        umls-similarity.pl program

          sim2r.pl                   -- converts umls-similarity.pl output to 
                                        a format that can be read by the R script
          create-icfrequency.pl      -- create the frequency file required for 
                                        information content measures

          create-icpropagation.pl    -- create the probability file required for 
                                        information content measures

  INSTALL
        To install these modules run:

          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

        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.

        To conduct an extensive test of the package please set the 
        UMLS_SIMILARITY_ALL_TESTS environment variable prior to 
        running make test. This will run the long tests:

        1. path-long.t
        2. ic-long.t
        3. relatedness-long.t

        To set the environment variable in c shell:

          setenv UMLS_SIMILARITY_RUN_ALL 1

        and in bash shell:

          export UMLS_SIMILARITY_RUN_ALL=1

  SOFTWARE COPYRIGHT AND LICENSE
        Copyright (C) 2004-2010 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.

  REFERENCING
        If you write a paper that has used UMLS-Similarity in some way, we'd 
        certainly be grateful if you sent us a copy and referenced UMLS-Interface. 
        We have a published paper that provides a suitable reference:

        @inproceedings{McInnesPP09,
           title={{UMLS-Interface and UMLS-Similarity : Open Source 
                   Software for Measuring Paths and Semantic Similarity}}, 
           author={McInnes, B.T. and Pedersen, T. and Pakhomov, S.V.}, 
           booktitle={Proceedings of the American Medical Informatics 
                      Association (AMIA) Symposium},
           year={2009}, 
           month={November}, 
           address={San Fransico, CA}
        }

        This paper is also found in
        <http://www-users.cs.umn.edu/~bthomson/publications/pubs.html>
        or
        <http://www.d.umn.edu/~tpederse/Pubs/amia09.pdf>

  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   Schütze 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.

        15  Patwardhan, S. and Pedersen T. Using WordNet Based Context Vectors 
            to Estimate the Semantic Relatedness of Concepts. In Proceedings of 
            the EACL 2006 Workshop Making Sense of Sense - Bringing Computational 
            Linguistics and Psycholinguistics Together, pp. 1-8, April 4, 2006, 
            Trento, Italy.

        16  Rada, R., Mili, H., Bicknell, E. and Blettner, M. Development and 
            application of a metric on semantic nets. In Proceedings of the 
            IEEE Transactions on Systems, Man, and Cybernetics, volume 19, 
            pages 17-30, 1989.

        17  Nguyen, H.A. and Al-Mubaid, H. New ontology based semantic 
            similarity mesaure for the biomedical domain. In Proceedings of 
            the IEEE International Conference on Granular Computing, pages 
            623-628, 2006.

  SEE ALSO
    <http://search.cpan.org/dist/UMLS-Interface>

    <http://search.cpan.org/dist/UMLS-Similarity>

  CONTACT US
    If you have any trouble installing and using UMLS-Interface, please
    contact us via the users mailing list :

    umls-similarity@yahoogroups.com

    You can join this group by going to:

    <http://tech.groups.yahoo.com/group/umls-similarity/>

    You may also contact us directly if you prefer :

      Bridget T. McInnes: bthomson at cs.umn.edu
      Ted Pedersen      : tpederse at d.umn.edu

  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

     Ying Liu, University of Minnesota
     liux0395 at umn.edu

  DOCUMENTATION COPYRIGHT AND LICENSE
    Copyright (C) 2003-2010 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.

