PubMed and Google Scholar: The Search For the Right Papers
Any researcher in the biomedical field can tell you that finding the right papers that discuss what you are interested in can become difficult. There are two overlying reasons for this. One, the searches we want are to broad. For example, if I want to know the affects of several inflammatory cytokines on the differentiation process of myoblasts, I could search “cytokine myoblast differentiation” in Google Scholar or Pubmed, a database of papers published in the biomedical sciences. However, the results of the search may be very different from what I actually need. The second reason the search could be confusing is the manner in which the search engine actually looks for resources. PubMed and Google Scholar use different algorithms when searching results for a given query. Using PubMed, “untagged terms that are entered in the search box are matched (in this order) against a MeSH (Medical Subject Headings) translation table, a Journals translation table, the Full Author translation table, Author index, the Full Investigator (Collaborator) translation table and an Investigator (Collaborator) index. When a match is found for a term or phrase in a translation table the mapping process is complete and does not continue on to the next translation table.” In other words, if I searched for “cytokine myoblast differentation”, the algorithm attempts to match the phrase with several databases and then attempts to match each term in the phrase along those databases. The search results are typically ordered in terms of “Relevance”, or how close the results were to matching the query (in our example, the list is ordered by descending normalized authority values). Google Scholar runs a similar algorithm but uses several more nonspecific databases and does not use the MeSH translations table. However, both run into the issue of getting results based on their authority values rather than how relevant they actually are to what is being searched for.
In class, we discussed how pages can be given an authority value such that pages with higher authority values have higher validity. However, just because a page has a high authority value does not mean that it is what is being requested. Thus, both PubMed and Google Scholar have options to narrow one’s search. By providing fields and tags, such as authors or providing papers that are similar to what we are looking for, the algorithm can calculate based on the more narrow query we throw at it. Consider a network of nodes representing Web pages. Each of these nodes has an authority value. Narrowing a search by providing tags and fields in Pubmed and Google Scholar is like removing several of the nodes and recalculating the authority values. By doing this, hub values also change, allowing the algorithm to narrow the results and reorder them such that the top results listed are more in line with what we are looking for.
The moral of the post: Instead of spending hours skimming over countless papers and realizing that most of them have nothing to do with your research, try taking two minutes of your time to narrow your search by adding tags and field values in advanced search options.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2000776/