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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Why Boolean strings don’t work

Why are Boolean strings so ubiquitous? Recruiters use them, lawyers use them, librarians use them, and many more. But as a universal format for expressing information needs, they leave a lot to be desired.

Which is why I recently posted an article called “This is why Boolean strings don’t work” on Medium. In that post, I outline three fundamental shortcomings inherent to the approach, and offer a call to action to consider alternatives. I hope you’ll join me in the pursuit of an alternative.

For further details, check out the post on Medium, or have a look at our alternative approach.

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Visualizing search strategies (part 2)

In our last post we reviewed some of the issues involved in developing effective solutions to complex search problems, and explored some of the challenges involved in formulating and representing Boolean strings and expressions. In particular, we explored the contribution of three experimental systems which aimed to offer an alternative to the conventional approach exemplified by line-by-line query builders and ‘advanced search’ forms. In this piece, we review some of the more recent examples, and reflect on the ways in which their ideas, insights and innovations may be productively applied to address contemporary search challenges.

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Visualizing search strategies (part 1)

According to the IDC whitepaper, The High Cost of Not Finding Information, knowledge workers spend 2.5 hours per day searching for information. Whether they eventually find what they are looking for or just stop and make a sub-optimal decision, there is a high cost to both outcomes. The recruitment industry, for example, relies on Boolean search as the foundation of the candidate sourcing process, and yet finding candidates with appropriate skills and experience remains an ongoing challenge. Likewise, healthcare information professionals perform painstaking searches of literature sources as the foundation of the evidence-based approach to medicine, but systematic literature reviews can take years to complete and new research findings may be published in the interim, leading to a lack of currency and potential for inaccuracy. Similarly, patent agents rely on accurate prior art search as the foundation of their due diligence process, and yet infringement suits are being filed at a rate of more than 10 a day due to the later discovery of prior art which their original search tools missed.

What these professions have in common is a need to develop search strategies that are accurate, repeatable and transparent. The traditional solution to this problem is to use line-by-line query builders which require the user to enter Boolean strings that may then be combined to form a multi-line search strategy:

However, such query builders typically offer limited support for error checking or query optimization, and their output is often compromised by mistakes and inefficiencies. In this post, we review three early but highly original and influential alternatives, and discuss their contribution to contemporary issues and design challenges.

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