Friday, 19 February 2016

Keywords, discoverability, and impact

 Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4511049/?log$=activity

J Med Libr Assoc. 2015 Jul; 103(3): 119–120.
PMCID: PMC4511049

Keywords, discoverability, and impact

Tanja Bekhuis, PhD, MS, MLIS, AHIP
Editors' Note: Keywords will improve the article impact and are now necessary for Journal of the Medical Library Association articles. Here is a brief editorial with background information.
When the editor of the Journal of the Medical Library Association (JMLA) asked me to write a piece about author keywords in MEDLINE structured abstracts [1],
the first thing I did was search for “keywords” in Google and Google
Scholar. This exercise was reminiscent of the lithograph Drawing Hands by M. C. Escher [2].
Think about it. I used Google, an ├╝ber-web search engine, to write
keywords to find keywords. Google and Google Scholar returned a deluge
of information (721 million and 4.35 million hits, respectively). Not
surprisingly, at the top of the first page in Google were hits for
Google AdWords and their Keyword Planner Tool [3].
I then did more focused searches in the ACL Anthology, a digital archive of papers in computational linguistics, and in Scientometrics
or journals with similar coverage to confirm that keyword analysis is
thriving in the text-mining and bibliometrics communities; for example,
see Ventura and Silva [4] or Yao et al. [5].
Despite
the circularity of my initial searches and too narrow follow-up
attempts, I learned that the meaning of the concept varies depending on
the domain. For example, in the search engine optimization (SEO) domain,
keywords are terms that improve page rank. Shrewd selection and
placement of words or phrases visible to the user or buried in hypertext
markup language (HTML) can move a hit toward the top of a list returned
by a search engine. Regarding these terms, the world of SEO has some
curious neologisms, such as spamdexing, which refers to keyword
stuffing, search engine spam, or black-hat SEO [6].
In contrast, white-hat SEO is ethical; its practitioners eschew
black-hat techniques. In corpus linguistics, keywords discriminate
between collections of documents to identify what is unique about, say,
general versus scientific prose, or British versus American English [7]. Text miners and other computational scientists extract informative keywords to classify documents or improve retrieval.
This brings us to why you, as an author, should carefully consider the list of keywords that you will assign to your JMLA
article and its relationship to your title and abstract. Think of
optimization principles for discoverability of your article beyond
MEDLINE and potential effects on the impact of your work. Overall,
enhancing discoverability of JMLA articles should improve
journal visibility, subsequent citation counts, and its impact. This is a
desirable outcome for you and the profession.
Discoverability
could depend on how well the title, abstract, and keyword list form a
miniaturized version of your paper. This is why a good structured
abstract resembles a paper written in the “Introduction, Methods,
Results And Discussion” (IMRAD) format (see Cooper's editorial in the
April 2015 JMLA). The title includes the most important
concepts in your paper and, ideally, the study design; the abstract
summarizes the components of your paper; and the keyword list includes
relevant concepts but with more detail than in the title. If keywords
are too broad or too narrow, they are useless. All three pieces are
important because web search engines and text-mining applications target
these sections and sometimes overweight text, depending on location.
Additionally, when presented to the reader, the title, abstract, and
keyword list must be laden with relevant information to capture
attention.
To write the keyword list for the JMLA,
channel your inner indexer. Select the Medical Subject Headings (MeSH)
that best characterize your topic to improve retrieval in MEDLINE [8].
Additionally, find words and phrases not covered by MeSH but known to
practitioners and researchers in your field. The MeSH terms you proffer
could improve decisions that a National Library of Medicine human
indexer makes—after all, you are likely to know more about the topic of
your paper than the indexer does. Adding non-MeSH terms could improve
discoverability of your article by web search engines and by users who
search digital repositories aside from PubMed and PubMed Central.
For example, in a recent paper we wrote for the JMLA on building gold standard datasets as a prelude to developing search filters [9],
my coauthors and I reported that “oral squamous cell carcinoma” is not
covered by MeSH, even though it is the most common cancer of the oral
cavity. However, the term is a synonym for “mouth squamous cell
carcinoma” in Emtree, the controlled vocabulary for Embase [10]. It also appears in the National Cancer Institute Thesaurus as “oral cavity squamous cell carcinoma” [11]. Any of these terms would have been good keywords for our paper.
In
sum, if terms from controlled vocabularies beyond MeSH seem useful,
consider adding them to your keyword list. Additionally, consider
free-text terms for which users are likely to search. By carefully
constructing your title, abstract, and keyword list, you will enhance
discoverability of your article and its potential impact.
figure mlab-103-03-119-120-uf01

REFERENCES

1. US National Library of Medicine, National Institutes of Health . Structured abstracts [Internet] Bethesda, MD: The Library [cited 18 Nov 2014] < http://www.nlm.nih.gov/bsd/policy/structured_abstracts.html>.
2. Escher MC. Drawing hands [Internet] Lithograph, 332mm × 282mm. 1948 [cited 25 Feb 2015]. < http://www.mcescher.com/gallery/most-popular/drawing-hands/>.
3. Google Ads Google AdWords keyword planner [Internet]. Google. 2015. [cited 25 Feb 2015]. < https://adwords.google.com/KeywordPlanner>.
4. Ventura
J, Silva J. Automatic extraction of explicit and implicit keywords to
build document descriptors. In: Correia L, Reis LP, Cascalho J, editors.
Progress in artificial intelligence. Springer; 2013. pp. 492–503.
5. Yao
Q, Chen K, Yao L, Lyu PH, Yang TA, Luo F, Chen SQ, He LY, Liu ZY.
Scientometric trends and knowledge maps of global health systems
research. Health Res Policy Syst. 2014 Jun 5;12(1):26. [PMC free article] [PubMed]
6. Weideman M. Website visibility: the theory and practice of improving rankings. Chandos Publishing, Elsevier; 2009.
7. McEnery T, Hardie A. Corpus linguistics: method, theory and practice. Cambridge, UK: Cambridge University Press; 2011.
8. US National Library of Medicine, National Institutes of Health . MeSH: Medical Subject Headings [Internet] Bethesda, MD: The Library [cited 28 Oct 2014] < http://www.nlm.nih.gov/mesh/>.
9. Frazier
JJ, Stein CD, Tseytlin E, Bekhuis T. Building a gold standard to
construct search filters: a case study with biomarkers for oral cancer. J Med Lib Assoc. 2015 Jan;103(1):346–54. DOI:http://dx.doi.org/10.3163/1536-5050.103.1.005. [PMC free article] [PubMed]
10. Elsevier BV; What is Emtree? [Internet] [cited 28 Oct 2014]. < http://www.elsevier.com/online-tools/embase/about/emtree>.
11. US National Cancer Institute . NCI thesaurus [Internet] Bethesda, MD: The Institute [cited 28 Oct 2014]. < http://ncit.nci.nih.gov>.

Articles from Journal of the Medical Library Association : JMLA are provided here courtesy of Medical Library Association


Keywords, discoverability, and impact

No comments:

Post a Comment