UMLS::Similarity
  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.

  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.

  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

        The package contains a web/ directory which contains a web interface to 
        the UMLS-Similarity package once it is installed. Please see the README 
        file in the web/ directory for further information.

  CONFIGURATION
    UMLS-Interface allows information to be extracted from the UMLS given a
    specified set of sources and relations through the use of a
    configuration file.

    There are six configuration options: SAB, REL, RELA, SABDEF, RELDEF, and
    RELADEF.

    The SAB and REL options are used to determine which sources and
    relations the path information is to be obtained from. The RELA option
    narrows down the relation even further. The RELA will only be applied to
    the PAR/CHD and RB/RN relations.

    The SABDEF and RELDEF options are used to determine which sources and
    relations to use when creating the Extended Definition. The RELA option
    narrows down the relation even further. The RELADEF will only be applied
    to the PAR/CHD and RB/RN relations.

    The path, wup, lch, lin, jcn and res measures require the SAB and REL
    options to be set. There is also an optional RELA option.

    The vector and lesk measures require the SABDEF and RELDEF options to be
    set with an optional RELADEF.

    You can specify a single source, multiple sources or the entire UMLS
    (using the UMLS_ALL option). Keep in mind that the greater the number of
    sources the larger the search space so if you obtaining path information
    about two concepts this will take longer. The names of the sources in
    the configuration file are expected to be in the SAB (source
    abbreviation) form. A listing of the sources and their SABs can be
    found:

    <http://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/re
    lease/source_vocabularies.html>

    You can specify any relations that exist in the specified set of sources
    that you defined. The directional (hierarchical) relations though are
    PAR/CHD and RB/RN. The other relations (such as RO and SIB) are not
    directional which means when obtaining path information when using these
    relations may take much longer than obtaining path information using the
    directional relations. A listing of the different relations can be found
    here (scroll down to the REL table):

    <http://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/re
    lease/abbreviations.html>

    If you do plan on using a multiple sources or the entire UMLS, we would
    advise you to use the --realtime option which is explained below, in the
    Interface.pm documentation and the path programs in the utils/
    directory. We also have a am UMLS_ALL option for this so you do not have
    to specify each and every source and relation.

    The format of the configuration file is as follows:

    SAB :: <include|exclude> <source1, source2, ... sourceN>

    REL :: <include|exclude> <relation1, relation2, ... relationN>

    RELA :: <include|exclude> <rela1, rela2, ... relaN>

    For example, if we wanted to use the MSH vocabulary with only the RB/RN
    relations, the configuration file would be:

     SAB :: include MSH
     REL :: include RB, RN

    or

     SAB :: include MSH
     REL :: exclude PAR, CHD

    If we wanted to use the SNOMEDCT vocabulary with only the PAR/CHD
    relations that are is-a relations, the configuration file would be:

     SAB :: include SNOMEDCT
     REL :: include PAR, CHD 
     RELA :: include isa, inverse_isa

    The format for SABDEF and RELDEF is similar.

    The SABDEF and RELDEF options are used to determine the sources and
    relations the extended definition is to be obtained from.

    The format of the configuration file is as follows:

    SABDEF :: <include|exclude> <source1, source2, ... sourceN>

    RELDEF :: <include|exclude> <relation1, relation2, ... relationN>

    RELADEF :: <include|exclude> <rela1, rela2, ... relaN>

    Note: RELDEF takes any of MRREL relations and two special 'relations':

          1. CUI which refers to the CUIs definition

          2. TERM which refers to the terms associated with the CUI

    For example, if we wanted to use the definitions from MSH vocabulary and
    we only wanted the definition of the CUI and the definitions of the CUIs
    SIB relation, the configuration file would be:

     SABDEF :: include MSH
     RELDEF :: include CUI, SIB

    If you wanted only the PAR/CHD definitions which are is-a relations.

     SABDEF :: include MSH
     RELDEF :: include PAR, CHD
     RELADEF :: include isa, inverse_isa

    For all of these options, there is an UMLS_ALL tag. If used with SAB or
    SABDEF, it would include all of the UMLS sources. If used with the REL
    or RELDEF, it would include all of the possible relations (as well as
    CUI and TERM for RELDEF). If used with the RELA or RELADEF, it would
    include all of the RELA relations including those with no RELA relation.
    Note that this is also the default for this option which is why it is
    optional. An example of using the UMLS_ALL option is as follows:

     SAB :: include UMLS_ALL
     REL :: include UMLS_ALL

    and another is:

     SABDEF :: include UMLS_ALL
     RELDEF :: include UMLS_ALL

    If you go to the configuration file directory, there will be example
    configuration files for the different runs that you have performed.

    For more information about the configuration options please see the
    README.

  PROPAGATION
    The Information Content (IC) is defined as the negative log of the
    probability of a concept. The probability of a concept, c, is determine
    by summing the probability of the concept occurring in some text plus
    the probability its descendants occurring in some text.

    The following is an example of the method UMLS-Interface uses to
    propagation counts to determine the probability of a concept in the
    sources/relations specified in the configuration file. In this method,
    we percolate the counts up the hierarchy, and in the case of multiple
    inheritance, we send a full count up all the paths to the parent.

    The icfrequency file contains the frequency of the following concepts
    existing in some corpus. For example, our corpus consists of three
    concepts, A B & C, each occurring five times:

     SAB :: (include|exclude) <sources>
     REL :: (include|exclude) <relations>
     N :: 15
     A<>5
     B<>5
     C<>5

    In this case, our sources and relations consist of the following
    'graph': Notation....A->D means A is a child of D....

     A->D
     B->D
     B->E
     D->F
     C->E
     E->F

    So A B and C are "leaf" nodes and F is the root.

    Step 1: determine the descendants of each nodes

     Descendants(A) = {}
     Descendants(B) = {}
     Descendants(C) = {}
     Descendants(D) = {A, B}
     Descendants(E) = {B, C}
     Descendants(F) = {A, B, C, D, E, F}

    Step 2: determine the probability of a concept, P(c), occurring by
    summing the probability of each of descendants plus its probability.

     P(A) = freq(A) / N = .33
     P(B) = freq(B) / N = .33
     P(C) = freq(C) / N = .33
     P(D) = (freq(A)+freq(B)+freq(D)) / N = .66
     P(E) = (freq(B)+freq(C)+freq(E)) / N = .66
     P(F) = (freq(A)+freq(B)+freq(C)+freq(D)+freq(E)+freq(F)) / N = .99

    Step 3: print the probability of the concept occurring, P(c), for each
    node in the sources/relations defined in the configuration table.

     SMOOTH :: 0 <- or 1 if smoothing was used
     SAB :: (include|exclude) <sources>
     REL :: (include|exclude) <relations>
     RELA :: (include|exclude) <relas>  <- if any are specified in the config
     N :: 15
     A<>.33
     B<>.33
     C<>.33
     D<>.66
     E<>.66
     F<>.99

    The information content for the nodes is then calculated by taking -log
    of this probability.

    We have an option that incorporates Laplace smoothing. Laplace smoothing
    is where the frequency count of each of the concepts in the taxonomy is
    incremented by one. The advantage of doing this is that it avoids having
    a concept that has a probability of zero. The disadvantage is that it
    can shift the overall probability mass of the concepts from what is
    actually seen in the corpus.

  SOFTWARE COPYRIGHT AND LICENSE
        Copyright (C) 2004-2011 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 Fransisco, 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) 2003-2011 Bridget T. McInnes, Siddharth Patwardhan,
    Serguei Pakhomov, Ying Liu 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.

