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), Zhong, et al. (2002), 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/zhong.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 UMLS/Similarity/lesk.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. 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 for information content measures create-icpropagation.pl -- create the probability file for information content measures vector-input.pl -- script to create the matrix and index files for the vector measure 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: 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): 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 :: REL :: RELA :: 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 :: RELDEF :: RELADEF :: 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 propagate 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) REL :: (include|exclude) 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) REL :: (include|exclude) RELA :: (include|exclude) <- 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 or 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 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: 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) 2003-2013 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: and is included in this distribution as FDL.txt.