text mining analyses often involve estimating the similarity of two terms or 
 concepts . in the biomedical domain ,
medline records include manual indexing by 
 experts of topics discussed in each article , using a standardized hierarchical 
 terminology of medical subject headings ( mesh terms ) that is employed to assist in 
 retrieval of articles on a given topic .
various schemes have been proposed for 
 relating different mesh terms to each other in terms of their similarity . in 
 general
, these schemes can be classified as a ) semantic , e.g. , the path distance 
 separating the two mesh terms on the hierarchical tree ; b ) contextual , e.g. , to what 
 extent the two mesh terms co - occur within the same articles ; and c ) lexical , e.g. , 
 the edit distance involved in transforming one term into another ( zhou et al , 2015 ) .
co - occurring mesh terms have 
 been studied as an indicator of relations discussed in articles ( e.g. , burgun and bodenreider , 2001 ; srinivasan and hristovski , 2004 ; kastrin et al , 2014 ) and mesh - based similarity 
 metrics have been employed in clustering of topically related articles ( e.g. , lee et al , 2006 ; zhou et al , 2009 ; boyack et al , 2011 ) .
several text mining models 
 devoted to literature - based discovery have utilized similarity of two mesh terms , or 
 of two umls concepts , as features ( e.g. , cohen et al , 
 2010 ; theodosiou et al .
, 2011 ; 
 workman et al . , 2013 , 2015 ) . in the present work ,
the first involves calculating how often two different mesh 
 terms co - occur in the same articles , relative to the expected chance level ( i.e. , 
 due to the frequencies of each mesh term considered independently ) .
we confirm that 
 this metric captures topical similarity as judged by human raters , and point out 
 some potential new uses for the metric in text mining .
the second metric is novel : 
 how often two different mesh terms co - occur in the body of articles written by the 
 same individual , relative to the expected chance level .
as we will show , this 
 author - based metric has potential value for author name disambiguation modeling . 
 both person - centered and article - centered metrics are being released openly as 
 comprehensive datasets and
article - based metric . for each article included in the 2014 baseline 
 version of medline , we extracted the medical subject headings ( mesh ) indexed in the 
 medline record , and calculated the number of times that each pair of mesh terms 
 co - occurred within the same article , as well as the total number of articles in 
 which each mesh term occurred .
a stoplist of the 20 most frequent mesh terms ( dsouza and smalheiser , 2014 ) was 
 employed to remove them from consideration , since highly frequent terms would appear 
 to be similar to all other mesh terms . only those mesh terms appearing in at least 
 25 articles were considered in calculating term similarity measures and odds ratios , 
 since lower values would be highly subject to noise . the final number of included 
 mesh terms is 25,548 .
the 2009 author - ity dataset ( torvik et al , 2005 ; torvik and 
 smalheiser , 2009 ) is based on a snapshot of pubmed ( which includes both 
 medline and pubmed - not - medline records ) taken in july 2009 , including a total of 
 19,011,985 article records , 61,658,514 author name instances and 20,074 unique 
 journal names .
each instance of an author name is uniquely represented by the pmid 
 and the position on the paper ( e.g. , 10786286_3 is the third author name on pmid 
 10786286 ) .
thus , each predicted author - individual cluster is associated with a list 
 of predicted pmids written by that individual . for each author - individual cluster 
 included in 2009 version of author - ity , we extracted the mesh terms found in each 
 cluster ( each term is counted once in each cluster , regardless of how many articles 
 it appeared in within that cluster ) . (
note that articles not in medline do not have 
 mesh indexing , and so even though not - medline articles were included in the 
 author - ity dataset , they did not contribute to the metric described here . )
we then 
 calculated the number of times that each pair of mesh terms co - occurred within the 
 same author - individual cluster , across all clusters in the dataset .
only mesh terms 
 that were included for calculating article - based similarity ( see above ) were 
 considered for calculating author - based similarity ; a total of 25,007 mesh terms 
 were included in the author - based metric .
the 
 number of pairs calculated for author metric is greater than included in the article 
 metric , since mesh terms were counted as co - occurring if they were mentioned in any 
 articles written by a given individual , even if they never co - occurred in the same 
 article .
conversely , the article metric contains 729,894 pairs that are not included 
 in the author - based metric ( i.e. , involving mesh terms which were added to medline 
 after 2009 ) .
finally , 36,655,958 pairs of mesh terms were included in both author 
 and article similarity metrics , and could be directly compared to see how the two 
 metrics capture different aspects of similarity .
, the number of 
 co - occurrences needs to be normalized by the total number of occurrences of each 
 mesh term , in order to assess properly how meaningful it is to find two terms 
 co - occurring ( in the same article , or in the set of articles published by a given 
 author ) .
two very common mesh terms might be expected to co - occur often just by 
 chance , whereas it will be highly significant if one observes any co - occurrence of 
 two very rarely occurring mesh terms .
we computed the co - occurrence score that would 
 be expected simply by chance ( for two mesh terms of their size ) , separately for the 
 article - based and author - based metrics .
this was done by ranking all mesh pairs by 
 the geomean of their individual document occurrences , dividing into bins of 5,000 
 pairs ( i.e. , each having roughly the same size ) , and calculating the average 
 co - occurrence score across all mesh pairs in the same bin . finally , we calculated 
 the mesh odds ratio for each pair of mesh terms present in that bin , by taking the 
 observed co - occurrence score divided by the average co - occurrence score for that 
 bin .
this is similar to the manner in which odds ratios were computed for journal 
 similarity metrics in dsouza and smalheiser 
 ( 2014 ) .
( note the author - based metric described in the present paper 
 relates any two mesh terms according to how likely they are to appear 
 in the articles written by the same author .
in contrast , the author - based metric in 
 dsouza and smalheiser ( 2014 ) 
 relates any two journals according to how likely they are to appear in 
 the articles written by the same author . )
we employed correlation measures to characterize the relationship between two 
 metrics , which allowed us to estimate the similarity of the metrics . in general , the 
 nonparametric spearman rho rank correlation coefficient is more appropriate for 
 these comparisons , because the metrics are generally not linear .
however , we also 
 present the parametric pearson r correlation coefficient as well , since there is 
 some value in comparing the pearson and spearman values ( e.g. , if both are high , the 
 relationships are likely to be linear , whereas if pearson is very low and spearman 
 is very high , the relationships are likely to be nonlinear ) . because each 
 correlation was computed across millions of data points , statistical significance is 
 generally extremely high and p - values are not displayed .
as one might expect , the article - based and author - based mesh odds ratios were 
 significantly correlated , but perhaps surprisingly , the correlations were only about 
 0.5 ( pearson r = 0.501 , spearman rho = 0.558 ) .
in other words , the two metrics do 
 not simply measure the same thing .
rather , the tendency of two mesh terms to 
 co - occur in the same article reflects somewhat different aspects of similarity than 
 the tendency of the same mesh terms to co - occur within the body of work published by 
 the same author .
the article - based metric , which counts co - occurrence of two mesh terms in the same 
 article , is subject to some limitations and constraints since a single article tends 
 to have only 8 - 20 mesh terms , and since medline indexers follow complex rules by 
 which they decide to pick a given mesh term ( e.g. , if more than one term is 
 applicable but they lie vertically within the hierarchical tree , they are instructed 
 to choose only the most specific term ) .
the co - occurrence of two mesh terms within 
 the same article might be expected to identify pairs that show semantic similarity 
 ( e.g. substantia nigra and neostriatum ) as well as pairs exhibiting more general 
 relatedness that is associated with function , proximity or usage ( e.g. , substantia 
 nigra and parkinsonian disorders ) .
pedersen et al 
 ( 2007 ) compiled a list of 29 umls concept ( cui ) pairs annotated by 
 physicians on a 1 to 4 scale of semantic similarity ( table 1 ) .
we mapped these to the corresponding mesh term pairs as far as 
 possible , and found that physician ratings correlated very well with the 
 article - based metric ( r = 0.67 ) .
a similar finding was observed with ratings by 
 medical coders ( table 1 ) .
pedersen et al ( 2007 ) compiled a list 
 of 29 umls concept ( cui ) pairs annotated by physicians or medical coders , on 
 a 1 to 4 scale of semantic similarity .
we mapped these to the corresponding 
 mesh term pairs as far as possible and displayed their article - based and 
 author - based odds ratios .
in contrast , these ratings showed a much lower correlation with the author - based 
 metric ( r = 0.38 ) . note that one of the test pairs ( cholangiocarcinoma and 
 colonoscopy ) co - occurred relatively infrequently within the same article ( odds ratio 
 = 0.44 ) , but had a high author - based odds ratio (= 8.02 ) , indicating that certain 
 individuals , presumably gi specialists , tended to publish on both topics .
seven of 
 the mesh pairs in table 1 had no 
 co - occurrences at all within the same article ( and hence have article - based 
 similarity scores of 0 ) , yet all of these had author - based co - occurrences such that 
 the odds ratios were greater than zero .
this may suggest that the author - based 
 metric is more sensitive in detecting weak relationships .
if an author has published 7 articles , and 
 each has 8 mesh terms , potentially there is a pool of 56 mesh terms to be considered 
 pairwise , compared to only 8 mesh terms for each article .
this makes the author 
 metric relatively robust and less influenced by fluctuations due to low sampling , 
 particularly for mesh terms that occur in relatively few articles . for any given 
 mesh term ,
its article - based odds ratio tended to achieve higher maximal values than 
 did the author - based odds ratios ( article - based maximal odds ratio = 73.525 mean 
 + 52.16 sd vs. author - based maximal odds ratio = 49.213 
 mean + 36.11 sd , a difference that is highly significant 
 ( p < 0.0001 , one - tailed unpaired t - test ) ) .
one way to compare the article - based and author - based metrics is to examine the 
 datasets as they can be queried on the arrowsmith project mesh pair metrics page 
 http://arrowsmith.psych.uic.edu/arrowsmith_uic/mesh_pair_metrics.html . 

the user selects any mesh term from a drop - down menu , and the site displays the top 
 20 most related mesh terms ranked according to either the article - based or 
 author - based metrics ( table 2 ) .
for each mesh 
 pair , the site also displays the number of articles in which each mesh term occurs , 
 the number of co - occurrences ( in articles or author - individual clusters ) , the 
 average number of co - occurrences expected for two mesh terms by chance ( based on 
 their size ) , and the calculated odds ratio .
it is interesting to view how the 
 article - based and author - based metrics sometimes emphasized different dimensions of 
 similarity .
for example , consider the top 20 terms related to the mesh term 
  tennis  ( table 3 ) .
the 
 article - based metric lists 8 terms related to physical therapy and disorders that 
 affect tennis players ( vs. 4 terms listed under the author - based metric ) , whereas 
 the author - based metric listed 10 other sports ( vs. 5 sports listed under the 
 article metric ) .
simply put , articles on tennis talked more about disorders 
 afflicting tennis players , and did not generally include other sports in the same 
 articles , whereas authors who wrote about tennis wrote more often about a variety of 
 other sports .
the top 20 terms according to 
 the article - based metric included 7 terms that were related to nursing and 
 medications ( vs. 2 listed under the author - based metric ) , whereas the author - based 
 metric included 14 terms related to information science ( vs. 8 under the 
 article - based metric ) .
shown are the top 20 mesh terms that co - occurred with  clergy  
 [ mesh ] , article count = 1641 , ranked by article odds ratio .
columns at left show the top 20 mesh terms that co - occurred with 
  tennis  ranked by article odds ratio . at right ,
. columns at left show the top 20 mesh terms that co - occurred with 
  abbreviations as topic  ranked by article odds ratio . at 

the present paper describes and provides comprehensive article - based and author - based 
 similarity metrics for pairs of medical subject heading ( mesh ) terms .
we also 
 present a web query interface that allows users to retrieve , for any specified mesh 
 term , the top 20 most related mesh terms according to either the article - based or 
 author - based metric . as discussed in the introduction ,
article - based mesh term pair similarity has been 
 previously discussed by others and utilized in studies of information retrieval , 
 document clustering , and literature - based discovery . here , we have confirmed that 
 the article - based metric does correspond to judgments of semantic relatedness made 
 by human raters .
we note that indexing an article with two mesh terms suggests that 
 the article may discuss a potential relation between the two topics  

especially if the article - based odds ratio of that pair is greater than 1 , i.e. , if 
 the two mesh terms co - occur in the same articles more often than expected by chance . 

we deem a mesh term pair as  important  if their article odds ratio 
 > 1 .
this further suggests a new kind of similarity metric for relating different 
 articles to each other .
that is , for any pair of articles , one can score the number 
 of  important  mesh term pairs that they share , perhaps weighted so 
 that mesh term pairs with higher odds ratios count more . counting mesh term pairs 
 will be more stringent and restrictive than counting individual shared mesh 
 terms .
the author - based mesh term pair similarity measures the tendency of the same 
 individual to discuss two different topics at some time during their career , i.e. , 
 in the body of articles that they have authored or co - authored .
this says as much 
 ( or more ) about the individual , and his or her range of interests , as it does any 
 overt relation between the two topics .
for example , the first author of this paper 
 ( ns ) has written on a variety of subjects ranging from extracellular matrix 
 biochemistry to natural language processing to a biography of the early 
 neuroscientist walter pitts .
the two mesh terms  dystroglycans  and 
  history , 20th century  are not obviously related to each other , and 
 in fact , do not co - occur in any single article in pubmed .
yet one might hypothesize 
 that an investigator who has worked on both topics might be psychologically or 
 otherwise better poised to detect new knowledge that bridges these two fields , or 
 that requires assembling different pieces of knowledge from each field , than someone 
 who has only worked in one field .
although novel discoveries often involve combining 
 topics together in new ways ( e.g. , uzzi et al , 
 2013 ; chen , 2014 ; mishra and torvik , 2014 ) , one can hypothesize 
 that mesh term pairs which do not co - occur at all in the same articles , yet have 
 high author - based odds ratios , may draw upon a pool of prepared minds ( to quote 
 pasteur ) and be particularly likely to be linked in new discoveries . those which 
 have very low author - based odds ratios might be less likely to be assembled into a 
 new finding by an individual investigator .
multi - disciplinary teams may have a 
 particular advantage for the investigation and discovery of findings that involve 
 putting together topics that have very low author - based odds ratios .
these 
 hypotheses are testable , and may further our understanding of why and how particular 
 novel combinations of topics lead to new discoveries .
our original reason for studying author - based mesh term similarity was to create an 
 additional feature that can be used to disambiguate author names on pubmed articles 
 comprehensively ( torvik et al .
, 2005 ; torvik and smalheiser , 2009 ) . the most relevant 
 measure of similarity for disambiguation is not topic centered , but rather 
 author - centered . for example , two journals may cover the same topic ( e.g. , 
 scandinavian journal of immunology vs. iranian journal of immunology ) , yet the same 
 person may have very little likelihood of publishing articles in both journals . 

thus , the 2009 author - ity disambiguation dataset was earlier mined to create a 
 metric that comprehensively measures the tendency of individuals to publish articles 
 in any two journals ( dsouza and smalheiser , 
 2014 ) . in the present paper ,
the author - based odds ratio was applied to pairs of mesh terms 
 to measure the tendency of an individual to publish a body of articles that is 
 indexed by any two given mesh terms during their careers .
we believe that the 
 author - based metric should have value in modeling author disambiguation . for 
 example ,
neuronal 
 growth cones : an extended view  ( pmid 2175018 ) is indexed with 10 mesh terms 
 ( plus two stoplisted terms ) ( table 5 ) , 
 whereas  microrna regulation of synaptic plasticity  ( pmid 19458942 ) 
 is indexed with 8 mesh terms ( plus one stoplisted term ) .
these two articles have no 
 mesh terms in common , so that a simple count of shared mesh terms would indicate no 
 similarity at all .
yet some of the mesh terms in the two articles are topically 
 similar , e.g. , neurons vs. neuronal plasticity .
our metrics are intended to quantify 
 how similar the pairs of mesh terms are .
neurons vs. neuronal plasticity shows high 
 article - based similarity ( odds ratio = 20.3 ) yet the most relevant measure is the 
 author - based similarity , which is somewhat less ( odds ratio = 3.1 ) .
if we were to 
 use the conventional article - based metric for the purpose of author disambiguation , 
 we would over - estimate the extent of similarity .
note that animals and humans are stoplisted and not used in calculating mesh 
 similarity metrics .
also , note that some of the mesh terms include 
 subheadings and are shown here , but subheadings were not considered in 
 calculating mesh similarity metrics . in order to utilize the author - based mesh similarity metric generally in comparing 
 two articles bearing the same name , one way to proceed is as follows : given two 
 articles sharing the same author ( lastname , firstinitial ) , we examine all mesh terms 
 for each article and consider the pairs that are formed across articles ( i.e. , one 
 mesh term in article 1 paired with another mesh term in article 2 ) .
the three 
 highest author - based odds ratios are averaged and used as the similarity score for 
 the two articles .
this feature is one of several new features ( e.g. , dsouza and smalheiser , 2014 ) that will 
 be added to existing features ( torvik et al , 
 2005 ; torvik and smalheiser , 2009 ) 
 that we are using to update and improve the performance of our author name 
 disambiguation model .
the datasets and readme.pdf are freely available for download from the arrowsmith 
 project website ( http://arrowsmith.psych.uic.edu/arrowsmith_uic/mesh_pair_metrics.html ) 
 as well as from the uic institutional repository , indigo , under the terms of the 
 creative commons attribution - noncommercial - sharealike cc by - nc - sa license 4.0 .
the 
 mesh term pair data is contained in mesh_pair_metrics.txt ( 16 gb uncompressed , 5 gb 
 compressed ) .
 in the present paper , we have created and characterized several similarity 
 metrics for relating any two medical subject headings ( mesh terms ) to each 
 other . 
 the article - based metric measures the tendency of two mesh terms to 
 appear in the medline record of the same article . 
 the author - based metric 
 measures the tendency of two mesh terms to appear in the body of articles 
 written by the same individual ( using the 2009 author - ity author name 
 disambiguation dataset as a gold standard ) . 
 the two metrics are only modestly 
 correlated with each other ( r = 0.50 ) , indicating that they capture different 
 aspects of term usage . 
 the article - based metric provides a measure of semantic 
 relatedness , and mesh term pairs that co - occur more often than expected by 
 chance may reflect relations between the two terms . 
 in contrast , the author 
 metric is indicative of how individuals practice science , and may have value for 
 author name disambiguation and studies of scientific discovery . 
 we have 
 calculated article metrics for all mesh terms appearing in at least 25 articles 
 in medline ( as of 2014 ) and author metrics for mesh terms published as of 2009 . 
 
 the dataset is freely available for download and can be queried at http://arrowsmith.psych.uic.edu/arrowsmith_uic/mesh_pair_metrics.html . 
 handling editor : elizabeth workman , mlis , phd . 
Background
Methods
Statistics
Results
Discussion
Implementation