Sunday, July 27, 2014

Size of Google Scholar vs other indexes, personally tuned discovery layers & other discovery news

Regular readers of my blog know that I am interested in discovery, and the role academic libraries should play in promoting discovery for our patrons.

If you feel the same, here are a mix of links I came across recently on the topic that might be of interest

The Number of papers in Google Scholar is estimated to be about 100 million

When talking about discovery one can't avoid discussion of Google Scholar. My last blog post on 8 surprising things I learnt about Google Scholar, raced to the top 20 all time read blog posts in just 3 weeks showing intense interest in this subject.

As such, the Number of Scholarly Documents on the Public Web is a fascinating paper that attempts to estimate the number of Scholarly documents on the public web using the capture/recapture method and in particular it gives you a figure for the number of papers in Google Scholar.

This is quite a achievement, since Google refuses to give this information.

It look me a while to wrap my head around the idea, but essentially it

  • It defines number of Scholarly documents on the web as the sum of the papers in Google Scholar (GS) and Microsoft Academic Search (MAS)
  • It takes the stated number of papers in  MAS to be a bit below 50 million.
  • It calculates the amount of overlap in papers found in both GS and MAS. This overlap needs to be calculated via sampling of course.
  • The overlap is calculated using papers that cite 150 selected papers. 
  • Using the Lincoln–Petersen method, the overlap of papers found and the given value of about 50 million papers in MAS , one can estimate the number of papers in Google Scholar and hence the total sum of papers on the public web. (You may have to take some time to understand this last step, it took me a while for sure)
There are other technicalities such as the paper estimates only English Language papers, being careful to sample papers with less than 1,000 cites (because GS allows only 1,000 results to be shown at most) .

For more see also How many academic documents are visible and freely available on the Web? which summarises the paper, and assesses the strengths and weaknesses of the methodology employed in the paper.

The major results are 

  1. Google Scholar has estimated 99.3 million English Language papers and in total there are about 114 million papers on the web (where web is defined as Google Scholar + MAS)
  2.  Roughly 24% of papers are free online
The figures here are figured to be a lower bound, but it is still interesting as it provides a estimate on the size of Google Scholar. Is 99.3 million a lot?

Here are some comparable systems and the sizes of indexes I am aware of as of July 2014. Scopes might be slightly different but will focus mostly on comparing scholarly or peer reviewed articles which are the bulk of most indexes anyway. I did not adjust for including English Language articles only though many of them do allow filtering for that. 
  • Pubmed - 20-30 million - the go to source for medical and life sciences area.
  • Scopus - 53 million  - mostly articles/conference proceedings but now include some book and book chapters. This is one of the biggest traditional library A&I databases, it's main competitor Web of Science is roughly at the same level but with more historical data , fewer titles indexed.
  • Base - 62 million -drawn from open access institutional repositories. Mostly but not 100% open access items and may include non-article times
  • CrossRef metadata Search - 67 million - Indexed dois - may include book or book chapters. 
So far these are around the level of Microsoft Academic Search at about 50 million.

Are there indexes that are comparable to Google Scholar's roughly 100 million? Basically the library webscale discovery services are the only ones at that level

  • Summon - 108 million - Scholarly material facet on + "Add beyond library collection" + authenticated = including restricted A&I records from Scopus, Web of Science and more. (Your instance of Summon might have more or less depending on A&I subscribed and size of catalogue, Institutional repositories). 
  • Worldcat - 2.1 billion holdings of which 148 million are peer reviewed, 203 million articles [as of Nov 2013]
I am unable to get at figures for the other 2 major library webscale discovery services - Ebsco Discovery Service and Primo Central, but I figure they should be roughly at the same level.

108 millions Scholarly material in Summon - may vary for your Summon Instance

  • Mendeley - 181 million ? This is an interesting case, Mendeley used to list the number of papers in their search but have removed it. The last figure I could get at is 181 million (from wayback machine), which fits with some of the statements made online but looks a bit on the high side to me. 

The figures I've given above with the exception of Mendeley I would think tends to be pretty accurate (subject to the issues of deduping etc) at least compared to the estimates given in the paper.

I think the fact that web scale discovery services are producing results in the same scale >100 million suggests that Google Scholar figure estimated is in the right ballpark. 

Still my subjective experience is that it seems that Google Scholar tends to have substantially more than our library web scale discovery service, so I suspect the 99.3 million obtained for Google Scholar is an underestimate. 

I wonder if one could use the same methodology as in The Number of Scholarly Documents on the Public Web to estimate the size of Google Scholar but using Summon or one of the other indexes mentioned above to measure overlap instead of Microsoft Academic Search.

There are some advantages

For example, there is some concern that the size of Microsoft Academic Search assumed in the paper to be 48.7 is not accurate but the figures given for say Summon are likely to be more accurate (again issues with deduping aside).

It would also be interesting to see how Google Scholar fares when compared to a index that is about twice as large as MAS.

Would using a web scale library discovery service to estimate the size of Google Scholar give a similar figure of about 100 million? 

Arguably not since we are talking about a different populations ie. MAS + GS vs Summon + GS though both can be seen as a rough estimate of the size of scholarly material available in the world that can be discovered online. (Also are the results you can find in Summon be considered the "public web" if you need to authenicate before searching to see a subset of results from A&I databases like Scopus?)

The main issue though I think to trying to use Summon or anything similar in place of MAS is a technical one.

The methodology measures overlap in a way that has been described as "novel and brilliant", instead of running the same query on the 2 searches and looking for overlaps, they do it this way instead.

"If we collect the set of papers citing p from both Google Scholar and MAS, then the overlap between these two is an estimate of the overlap between the two search engines." 

Unfortunately none of the web scale discovery services have a cited by feature (they do draw on and display Scopus and Web of Science cited counts but that's a different matter)

One can fall back on older methodologies and measuring overlap by running the same query on GS and Summon, but this has drawbacks described as "bias and dependence" issues. 

Boolean versus ranked retrieval - clarified thoughts

My last blog post Why Nested Boolean search statements may not work as well as they did was pretty popular but what I didn't realise that I was implicitly saying that relevance ranking of documents retrieved using Boolean operators did not generally work well.

This was pointed out by Jonas 

I tweeted back asking why we couldn't have good ranked retrieval on documents retrieved using Boolean operators and he replied that he thinks it's based two different mindsets and one should either "trust relevance or created limited sets."

On the opposite end, Dave Pattern of Huddersfield reminded me that Summon's relevancy ranking was based on Open Source Lucene software with some amount of tweaking. You can find some details  but essentially it is designed to combine Boolean with Vector Space models etc aka it is designed or can do Boolean + ranked retrieval.

After reading though some documentation and the excellent Boolean versus ranked querying for biomedical systematic reviews, I realized my thinking on this topic was somewhat unclear.

As a librarian, I have always assumed it makes too much sense to (1) Pull out possibly relevant articles using Boolean Operators (2) Rank them using various techniques from classic tf-idf factors to other more modern techniques like link popularity etc.

I knew of course, there were 2 paradigms, that the classic Boolean set retrieval assumed every result was "relevant" and did not bother with ranking beyond sorting by date etc. But it still seemed odd to me not to try to at least to add ranking. What's the harm right?

The flip side was, what is ranked retrieval by itself? If one entered SINGAPORE HISTORICAL BUILDINGS ARCHITECTURE, it would still be ranking all documents that had all 4 terms right?(maybe with stemming) or wasn't it really still Boolean with ranking?

The key I was missing which now seemed obvious is that for ranked retrieval paradigms not every search term in the query has to be matched.

I know those knowledgeable in information retrieval reading this might think this be obvious and I am dense for not realizing this. I guess I did know this except I am a librarian, I am so trapped into Boolean thinking that I assume implicit AND is the rule.

In fact, we like to talk about how Google and some web searches do "Soft AND", and kick up a fuss when they might sometimes drop off one or more search terms. But in ranked retrieval that's what uou do, you throw in a "bag of words" (could be a whole paragraph of words), the ranking algorithms tries to do the best it can but the documents it fulls up may not have all the words in the query.

Boolean versus ranked querying for biomedical systematic reviews is particularly interesting paper, showing how different search algorithms ranging from straight out Boolean to ranked retrieval techniques that involve throwing in Title,abstracts as well as hybrid techniques that involve combining Boolean with Ranked retrieval techniques fare in term of retrieving clinical studies for systematic reviews.

It's a amazing paper, with different metrics and good explaintion of systematic reviews if you are unfamiliar. Particularly interesting they compare Boolean Lucene results which I think give you a hint on how Summon might fair.

The best algorithm for ranking might surprise you.... 

Read the full paper to understand the table! 

Large search index like Google Scholar, discovery service flatten knowledge but is that a good thing?

Like many librarians, I have an obsession on the size of databases, but is that really important?

Over at Library Babel Fish, Barbara Fister on the Library isn't flat, worries that academic libraries' discovery services are "are (once again) putting too high a value on volume of information and too little on curation".

 She ends with the following questions

"Is there some other way that libraries could enable discovery that is less flat, that helps make the communities of inquiry and the connections between ideas easier to follow? Is there a way to help people who want to join those conversations see the patterns and discern which ideas were groundbreaking and significant and which are simply filling in the details? Or is curation and connection too labor-intensive and inefficient for the globalized marketplace of ideas?"

Which makes the next section interesting....

Library Top Trends - Personally tuned discovery layers 

Ken Varnum at the recently concluded LITA Top Technology Trends Sessions certainly thinks that what is missing in current Library discovery services is the ability for librarians to provide personally tuned discovery layers for local use.

He would certainly think that there is value in librarians, slicing the collections into customized streams of knowledge to suit local conditions. You can jump to his section on this trend here. Also Roger Schonfeld's
section on Anticipatory discovery for current awareness of new publications is interesting as well.

To Barbara Fister's question on whether curation is too labour intensive or inefficient, Ken would probably answer no, and he suggests that in the future librarians can customize collections based on subject as well as appropriateness of use (e.g Undergraduate vs a Scholar).

It sounds like a great idea, since Summon and Ebscohost discovery layers currently provide hardcoded discipline sets and I can imagine eventually been able to create subject sets based on collections at the database and/or at the journal title levels (shades of the old federated search days or librarians creating google custom search engines eg one covering NGO Sites or Jurn (open access in humanities)).

At the even more granular level, I suppose one could also pull from reading lists etc.

Unlike Ken though I am not 100% convinced though it would just take "a little bit of work" to make this worth while or at least better than the hardcoded discipline sets. 

NISO Publishes Recommended Practice on Promoting Transparency in Library Discovery Services

NISO RP-19-2014, Open Discovery Initiative: Promoting Transparency in Discovery [PDF] was just published.

Somewhat related is the older NFAIS Recommended practices on Discovery Services [PDF]

I've gone through it as well as EBSCO supports recommendations of ODI press release and I am still digesting the implications, but clearly there is some disagreement about handling of A&I resources (not that shocking).

Discovery Tools, a Bibliography

Highly recommend resource - this is a bibliography by Fran├žois Renaville. Very comprehensive covering papers from 2010 onwards.

It is a duplicate of the Mendeley Group "Libraries & [Web-Scale] Discovery Tools.

Ebsco Discovery Layer related news

Ebsco has launched a blog "Discovery Pulse" with many interesting posts. Some tidbits

Note : I am just highlighting Ebsco items in this post because of their new blog as the blog may be of interest to readers. I would be happy to highlight Primo, Summon, WorldCat discovery service items when and if I become aware of them. 

Summon Integrates Flow research management tool.

It was announced that in July, Summon will integrate with Proquest Flow, their new cloud based reference management tool.

The word Login is extremely misleading in my opinion. 

I have very little information about this and how overt the integration will be. But given that Mendeley was acquired by Elsevier, Papers by Springer, it's no wonder that Proquest wants to get into the game as well.

It's all about trying to get into the researcher's workflow and unfortunately as increasingly "discovery happens elsewhere", so it would be smart to focus on reference management an area where currently the likes of Google seem to be ignoring (though moves like Scholar Library where one can add citations found in Google Scholar to your own personal library may say otherwise).

Mendeley for certain has shown that reference management is a very powerful place to start to get a digital foothold.

While it's still early days, currently Flow seems to have pretty much the standard features one sees in most modern reference managers eg. Free up to 2GB storage, support of Citation Style Language (CSL), capabilities for collaboration etc. I don't see any distinguishing features or unique angles yet.

Here's a comparison in terms of storage space for the major competitors such as Mendeley.

The webinar I attended on it (sorry don't have link to recording) suggests Proquest has big plans for Flow, beyond a reference manager. It will aim to support the whole research cycle, and I think this includes support as a staging ground for publication (submission to PQDT??), as well as support of prepub works (posting to Institutional or Subject repositories?).

It will be interesting to see if Proquest will try to leverage it's other assets such as Summon to support Flow. Eg. Would Proquest tie recommender services drawn from Summon usage into it?

Currently you can turn off Flow from Summon without much ill effects and it seems some libraries have done so because it may take time to evaluate and prepare staff to support this, but it remains to see if in the long run , if Flow might just have too many features and value to be turned off.

BTW If you want to keep up with articles, blog posts, videos etc on web scale discovery, do consider subscribing to my custom magazine curated by me on Flipboard (currently over 1,200 readers) or looking at the bibliography on web scale discovery services)

Monday, July 14, 2014

Why Nested Boolean search statements may not work as well as they did

At library school, I was taught the concept of nested boolean. In particular, I was taught a particular search strategy which goes like this.

  • Think of a research topic
  • Break them up into major concepts - typically 3 or more - eg A, B, C
  • Identify synonyms for each concept (A1,A2, A3 ; B1, B2, B3 ; C1, C2, C3
  • Combine them in the following manner

(A1 OR A2 OR A3) AND (B1 OR B2 OR B3) AND (C1 OR C2 OR C3)

We like many libraries have created videos on it as well.

If you are a academic librarian who has even taught a bit of information literacy, I am sure this is something you show in classes. You probably jazzed it up by including wildcards (such as teen*) as well.

Databases also encourage this search pattern

I am not sure how old this technique is, but around 2000ish? databases also started to encourage this type of structured search.

Above we see Ebscohost platform and in my institution this "Advanced search" is set to default. You can see a similar UI (whether as default or advanced search) in JSTOR, Engineering Village, Proquest platforms etc.

A lecturer when I was in library school even claimed credit (perhaps jokingly) for encouraging databases into this type of interface.

Recently I noticed a slight variant on this theme where the default search would show only one search box (because "users like the Google one box" according to a webinar I attended), but if you clicked on "add field" or similar you would see a similar interface. Below shows Scopus.

After clicking Add search field, you get the familiar structured/guide search pattern

You see a similar idea in the latest refresh of Web of Science, a default single search box but with a option to expand it to a structured search pattern. Below we see Web of Science with "Add another field" selected twice.

Lastly even Summon 2.0 which generally has a philosophy of keeping things simple got into the act and from what I understand under pressure from librarians finally came up with a advanced search that brought tears of joy to power users. 

But are such search patterns really necessary or useful?

In the first few years of my librarianship career, I taught such searches in classes without thinking much of it. 

It feels so logical, so elegant, it had to be a good thing right? Then I began studying and working on web scale discovery services, and the first doubts began to appear. I also started noticing when I did my own research I rarely even did such structured searches.

I also admit to be influenced by Dave Pattern's tweets and blog posts, but I doubt I will ever be as strongly in the anti-boolean camp.

But I am going to throw caution to the wind and try to be controversial here and say that I believe increasingly such a search pattern of stringing together synonyms of concepts generally does not improve the search results and can even hurt them

There is of course value in doing this exercise of thinking through the concepts and figuring out the correct language used by Scholars in your discipline, but most of the time doing so does not improve the search results much especially if you are simply putting common variants of words eg different variants of say PREVENT or ECONOMIC which is what I see many searches do.

That's because many of the search systems we commonly use increasingly are no longer well adapted to such searches even though they used to be in the past

Our search tools in the past

Think back to the days of the dawn of the library databases. They were characterized by the following

  1. Metadata (including subject terms) + abstract only - did not including full text
  2. Precise searching - what you enter is what you get search
  3. low levels of aggregation - A "large database" would maybe have 1 million items if you were lucky
In such conditions, most searches you ran had very few results. If you were unlucky you would have zero results. 


Firstly the search matched only over metadata + abstract and not full text. So if you searched for "Youth" and it just happened that the abstract and title the author decided on using "Teenager", you were sunk.

Also this was compounded by the fact that in those days, searches were also very precise. There was no autostemming that automatically covered variants of words (including British vs American spelling), so you had to be careful to include all the variants such as plurals, and other related forms. 

Lastly, It is hard to imagine in the days of Google Scholar with estimated 100 million documents (and Web Scale discovery systems that could potentially match that) but in those days databases were much smaller and fragmented with much smaller indexes and as such the most common result would be zero hits or at best a few dozen hits.

Summon full index (Scholarly filter on) showing about 100 million results

This is why the (A1 OR A2 OR A3) AND (B1 OR B2 OR B3) AND (C1 OR C2 OR C3) nested boolean technique was critical to ensure you expanded the extremely precise search to increase recall.

Add the fact that search systems like Dialog were charged per search or on time - so it was extremely important to craft the near-perfect search statement in one go to do efficient searching.

I will also pause to note that relevancy ranking of results could be available but when you have so few results that you could reasonably look through say 100 or less, you would just scan all the results, so whether it was ranked by relevancy was moot really.

Today's search environment has changed

Fast forward to today.

Full-text databases are more common. In fact, to many of our users and younger librarians, "databases" would imply full-text databases and they would look in dismay when they realized they were using a abstract and indexing database and wonder why in the world people would use something that might not give them instant gratification of a full text item. I fully understood some old school librarians would consider this definition to be totally backwards but......

Also the fact you are searching full-text rather than just metadata changes a lot. If an article was about TEENAGERS, there is pretty good odds you could find TEENAGER and probably, YOUTH, ADOLESCENCE etc in the full text of the book or article as well, so you probably did not need to add such synonyms to pick them up in the result set anyway.

Moreover as I mentioned before , increasingly databases under the influence of Google are starting to be more "helpful", by autostemming by default and maybe even adding related synonyms, so there was no real need to add variants for color vs colour say or for plural forms anyway.

Even if you did a basic

A AND B AND C -  you would have a reasonable recall, thanks to autostemming, full text matching etc.

All this meant you get a lot of results now even with a basic search.

Effect of full-text searching + relative size of index + related words

Don't believe this change in search tools makes a difference? Let's try the ebscohost discovery service for a complicated boolean search because unlike Summon it makes it easy to isolate the effect of each factor.


Let's try this search for finding studies for a systematic review

depression treatment placebo (Antidepressant OR "Monoamine Oxidase Inhibitors"  OR "Selective Serotonin Reuptake Inhibitors" OR "Tricyclic Drugs") ("general  practice" OR "primary care") (randomized OR randomised OR random OR trial)

Option 1 : Apply related words + Searched full text of articles - 51k results

Option 2 : Searched full text of articles ONLY -  50K results

Option 3 : Apply related words ONLY - 606 results

Option 4 : Both off - 594 results 

The effect of apply related keywords is slight in this search example possibly because of the search terms used, but we can see full text matches make a huge difference.

Option 4 would be what you get for "old school databases". In fact, you would get less than 594 results in most databases, because Ebsco Discovery service has a huge index far larger than any such databases.

To check, I did an equivalent search in one of the largest traditional abstracting and indexing database Scopus and I found 163 results (better than you would expect based on the relative sizes of Scopus vs EDS).

But 163 is still manageable if you wanted to scan all results, so relevancy ranking can be poor and it doesn't matter as much really.

Web scale discovery services might give poor results with such searches 

I know many librarians will be saying, doing nested Boolean actually improves their search, and even if it doesn't what's the harm?

First, I am not convinced that people who say nested boolean improves the results of their search have actually done systematic objective comparisons or whether it is based on impression that I did something more complicated so the results must be better. I could be wrong.

But we do know that many librarians and experienced users are saying the more they try to carry out complicated boolean searches the worse the results seem to be in discovery services such as Summon.

Matt Borg of Sheffield Hallam University wrote of his experience implementing Summon.

He found that his colleagues reported "their searches were producing odd and unexpected results."

"My colleagues and I had been using hyper stylised searches, throwing in all the boolean that we could muster. Once I began to move away from the expert approach and treated Summon as I thought our first year undergrads might use it, and spent more time refining my results, then the experience was much more meaningful." - Shoshin

I am going to bet that those "hyper stylised searches" were the nested boolean statements.

Notice that Summon like Google Scholar actually fits all 3 characteristics of a modern search I mentioned above that are least suited for such searches
  • Full text search
  • High levels of aggregation (typical libraries implementing Summon at mid-size universities would have easily 300 million entries)
  • autosteming was on by default - quotes give a boost to results with exact matches.
All this combine to make complicated nested Boolean searches worse I believe.

Poor choices of synonyms and overliberal use of wildcards can make things worse

I will be first to say the proper use of keywords is the key to getting good results. So a list of drugs names combined by an OR function, or a listing of philosophers, concepts etc - association of concepts would possible give good results.

The problem here is that most novice searchers don't have an idea what are the keywords to list in the language of the field, so often because they are told to list keywords they may overstretch and add ones that make things worse.

Say you did

(A1 OR A2 OR A3) AND (B1 OR B2 OR B3) AND (C1 OR C2 OR C3)

Perhaps you added A3, B3, C3 though they aren't exactly what you are looking for but "just in case".

Or perhaps you decided it wouldn't hurt to be more liberal in the use of wildcards which led to matches of words you didn't intend. 

Or perhaps the keyword A3, B3, C3 might be used in a context that is less appropriate that you did not expect. Remember unlike typical databases, Summon is not discipline specific, so a keyword like "migration" could be used in different disciplines. 

The fact that web scale discovery searched through so much content, there would be a high chance of getting A3 AND B3 AND C3 entries that were not really that relevant when used in combination.

Even if all the terms you chose were appropriate, the fact that they could be matched in full text could throw off the result.

If A2 AND B2 AND C2 all appeared in the full text in an "incidental" way, they would be a match as well. Hence creating even more noise.

And when you think about it, the problems I mention will get even worse. as each of the keywords would be autostemmed (which may lead to results you don't expect depending on how aggressive autostemming is) exploding the results.

My own personal experience with Summon 2.0 is that often the culprit is the match in full-text. Poorly chosen "synonyms" could often surface and even be pushed up.

The "explosion" issues is worsen by full text matches in books

In Is Summon alone good enough for systematic reviews? Some thoughts.  , I was studying to see if Summon could be used for systematic reviews. A very important paper, pointed out that Google Scholar was a poor tool for doing systematic reviews, because of the lack of precision features like lack of wildcards, limited character length, inability to nest boolean more than 1 level etc, and I had speculated Summon lacking these issues would be a better tool.

Somewhat surprising to me was when I tried actually to do so.

Sometimes, when I did the exact same search statement in both Google Scholar and Summon, number of Summon results usually exploded, showing more results than Google Scholar!

Please note that when I say "exact same search statement" I mean that precisely.

So for example, one of the searches done in Google Scholar to look for studies was

depression treatment placebo (Antidepressant OR "Monoamine Oxidase Inhibitors" 
OR "Selective Serotonin Reuptake Inhibitors" OR "Tricyclic Drugs") ("general 
practice" OR "primary care") (randomized OR randomised OR random OR trial)

Google Scholar found 17k results, while Summon (with add results beyond library collection to get the full index) shows 35K. 

Why does Summon have more than double the number of results?  

This was extremely unexpected because we generally suspect Google Scholar has a larger index and Google Scholar is more liberal in interpreting search terms as they may substitute terms with synonyms, while Summon at best includes variant forms of keywords (plurals, british/amercian spelling etc

But If you look at the content types of the results of the 35k results you get a clue.

A full 22k of the 35k results (62%) are books! If you remove those than the number of results make more sense. 

Essentially books which can be indexed in full text have a high chance of been discovered since they contain many possible matches and this gets worse the more ORs you pile on. Beyond a certain point they might overwhelm your results.

It is of course possible some of the 22k books matched can be very relevant, but it is likely a high percentage of them would be glancing hits and if you are unlucky, other factors might push them up high. 

I did not even attempt to use wildcards to "improve" the results, even though they could work in Summon. When I did that the number of results exploded even more.

As an aside the Hathitrust people have a interesting series of posts on Practical Relevance Ranking for 11 Million Books, basically showing you can't rank books the same way you rank other materials due to the much longer length of the book.

The key to note is that you are no longer getting 50, 100 or even 200 results like in old traditional databases. You are getting thousands. So you can no longer look through all the results, you are totally at the mercy of the relevancy ranking...

The relevancy ranking is supposed to solve all this... and rank appropriately, but does it? Do you expect it to?

A extremely high recall but low precision (over all results), with a poor relevancy ranking makes a broken search. Do you expect the relevancy ranking to handle such result sets resulting from long strings of OR?

With so few users actually doing Boolean in web scale discovery (e.g this library found  0.07% of searches uses OR), should you expect discovery vendors to actually tune for such searches? 

Final thoughts

I am not going to say these types of searches are always useless in all situations, just that often they don't help particularly in cases like Google, Google Scholar, web scale discovery.

Precise searching using Boolean operators has it place in the right database. Such databases would include Pubmed - which is abstract only, allows power field searching, including a very precise MESH system to exploit. The fact that medical searches particularly systematic reviews require comprehensiveness and control is another factor consider.

I also think if you want to do such searches, you should think really hard on just adding one more OR or liberal use of wildcards "just in case". With web scale discovery services searching full-text, and autostemming, a very poor choice will lead to explosion of searches with combinations of keywords found that may not be what you expect.

A strategic use of keywords is the key here, though often for the novice searcher who doesn't know the area, he is as likely to come up with a keyword that might hurt as it might help initially. As such it is extremely important to stress the iterative nature of such searches, so as you figure out more of the subject headings etc you use them in your search.

Too often I find librarians like to give the impression they found the perfect search statement by magic on their first try, which intimidates users. 

I would also highly recommend doing field searches, or metadata only search options if available, if you try such searches and get weird results.

Systems like Ebsco discovery service give you the option to restrict searches to metadata only or not search in full text.

For Summon, if you expect a certain keyword to throw off the search a lot due to full-text matches, doing title/subject term/abstract etc only matches might overcome this.

Try for example


So what do you think? Do you agree that increasingly you find doing a basic search is enough? Or am I understating the value of a nested boolean search? Are there studies showing they increase recall or precision.

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