The objective of NLM's Indexing Initiative (II) is to investigate
methods whereby automated indexing methods partially or completely
substitute for current indexing practices. The project will be
considered a success if methods can be designed and implemented that
result in retrieval performance that is equal to or better than the
retrieval performance of systems based principally on humanly assigned
For more than 150 years, the National Library of Medicine has
provided access to biomedical journal literature through the
analytical efforts of human indexers. Since 1966, access has
been provided in the form of electronically searchable
document surrogates consisting of bibliographic citations,
descriptors assigned by indexers from the MeSH controlled
vocabulary and, since 1974, author abstracts of many, but not
all, items. The objective of the Indexing Initiative project work
is to investigate methods whereby
automated indexing methods partially or completely substitute
for current indexing practices. The project will be considered a success
if methods can be designed and implemented that result in retrieval
performance that is equal to or better than retrieval of citations
based on humanly assigned index terms.
A project of this scope necessarily involves the efforts of
many people. Original and Core Project
Team Members are from
several NLM divisions, including the LHNCBC, LO, and NCBI.
The project will assume the availability of free text in the form of
titles and abstracts but will also consider the increasing availability
of the full text of journal articles in electronic form.
The project will investigate concept-based indexing methods that go
well beyond automatic word-based
indexing (such as the inverted word index already part of MEDLINE).
As insights are gained throughout the project, current operational
processes or systems may be iteratively modified and improved in keeping
with those insights.
For a list of presentations and papers about the Indexing
Initiative, please visit our
NCBI's Entrez Search and Retrieval System allows
searching of several linked databases: PubMed, Nucleotide, Protein,
Structure, Genome, PopSet, OMIM, Taxonomy, Books, Probe Set,
and 3D Domains.
UMLS Knowledge Source Server
Adobe's free PDF reader "Acrobat Reader" is required for reading the papers available on this website.