The snakes and ladders of search query formulation

If you were tasked with designing a universal framework for search query formulation, where would you start? Well, you could start from a command-line paradigm. After all, that’s the approach adopted by most query builders and ‘advanced search’ forms. But convention aside, is that really the best place to start? Personally, I am not convinced – IMHO command line approaches reflect the days when searches were conducted via remote terminals to subscription databases, and in that respect, they represent the past, not the future. Moreover, using Boolean strings to articulate complex information needs suffers from a number of fundamental shortcomings, in particular regarding scalability, efficiency and transparency. So what’s the alternative?

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Towards transparency in systematic searching

A couple of days ago I published a piece on Medium called “Searching PubMed just got smarter“, in which I talked about how we’ve added PubMed integration to 2dSearch, thereby offering a tool of immediate utility to anyone wishing to search MEDLINE in a systematic manner. The service has only been live a week or two, but the post did seem to strike a chord with a number of people. And some of that commentary got me thinking about various issues around reproducibility (with regard both to scientific research and to searching methodologies). But it also raised an issue around transparency, and the degree to which the formalisms we use to express information needs can scale to accommodate new use cases and requirements.

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A universal framework for information needs

A couple of days ago I published a piece on Medium called “Towards a universal language for search“, in which I talked about how we’ve added Google integration to 2dSearch, thereby allowing a user to apply the same search strategy (or Boolean string) to search multiple databases. Of course, this may not matter much to your ‘average web searcher’, but for some professions, this is a big thing. Now the point isn’t so much about Google (or Bing for the matter), that just happens to be the instance we have used to illustrate the concept. And crucially, the point isn’t about query languages either (in the programmatic sense) – important though they are, converting a user’s information need into a API call is a different problem.

Instead, what we’re contemplating here is the prospect of a universal framework for information needs. 

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BS in, wisdom out

Over the last few weeks I’ve been sharing various ideas about ways in which techniques from the field of data visualisation can be applied to help solve complex search problems, with particular focus on the process of query formulation. In those posts, we’ve discussed the scientific (and, one might argue, commercial) rationale for adopting such techniques in the development of future search strategies.

But what we haven’t really considered thus far is legacy content – in particular, the many archive collections of solutions to common search problems that are stored as curated collections of Boolean strings and search filters. These repositories offer a vital source of inspiration and guidance and play a key role in the propagation of knowledge and best practice for a variety of professions.

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Field tags, search, and universal formalisms

One small step for an app, one giant leap for advanced search… well, maybe not. But introducing field tags on 2dSearch is potentially more significant than it sounds, so I hope you’ll indulge my reflection for a moment here.

Superficially, we’ve added a way for user to articulate commonly used search operators such as:

• ‘site:linkedin.com’ (i.e. search for webpages from the site ‘linkedin.com’)
• ‘intitle:profile’ (i.e. return pages that have ‘profile’ in the title).

This would be rendered in the UI as follows:

So far, so simple. But what we’re doing is actually much more than that: we are providing a generic mechanism to differentiate between content and metadata, and have the system interpret that construction according to the semantics of the underlying database. This has far broader implications.

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Advanced search needs advanced suggestions

Do you use search suggestions on Google? I’ll wager you do: they save time, help to minimise error-prone keystrokes, and even if not consciously processed, can give us the inspiration we need to form better queries than we’d originally contemplated.

But query suggestions, as we currently understand them, have their limitations. In particular, they are predicated on the assumption that most queries are composed of relatively short sequences of keywords, perhaps with some elementary linguistic structure. This is of course largely true for web search, but for many advanced or professional search applications, the assumption breaks down.

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Rethinking ‘advanced search’

When was the last time you used ‘advanced search’ on a website or information resource? Go on, admit it – unless you’re a professional searcher, the answer is probably measured in months (if not years). Why is this? Partly, it might be that the concept itself is flawed, on the basis that an effective search experience should place ‘advanced’ search tools in the hands of all users as and when they are able and willing to use them.

But mostly, I’d wager, it’s down to execution. Invariably, instances of advanced search are predicated on the notion of either parametric search or Boolean string search (or both), with all their flaws and idiosyncrasies. Inevitably, most users vote with their feet, but advanced search is retained as a concession to a more vocal group of ‘power users’. Not that their needs are not legitimate, of course – quite the contrary – but my point here is that support for that requirement and the requirement itself are two very different things.

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