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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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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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A while ago I wrote a post discussing some of the shortcomings of current approaches to search; focusing on the relatively primitive and inefficient formalisms used by many database vendors to express search strategies. In that post, I argued that the conventional approach shares many of the shortcomings of early programming languages such as first generation Basic, relying on arbitrary labels such as line numbers to convey structure and organisation.

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In our last few posts we’ve discussed a range of topics that have explored some of the complexity and nuance in designing effective search experiences. However, much of this complexity is hidden from the majority of us, as its effect is most keenly felt in what we call ‘professional search’ applications, i.e. those used by lawyers, scientists, information professionals, etc. in the course of their work. These individuals undertake search tasks as part of their professional duties, often applying specialist expertise to address a specific brief, with a high value outcome and within explicit resource constraints.

So I am delighted therefore to invite you to attend the First International Workshop on Professional Search, which will be held on 12 July in Ann Arbor, Michigan as part of SIGIR 2018:

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IRSG logoJust in case you missed it, here are details of the latest issue of Informer, which came out on this week. As usual, lots of good stuff, with a mix of conference reviews, feature articles, news and updates in the world of IR. For further details see the Informer website. Or if you fancy becoming a contributor, get in touch!

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[Image courtesy of pexels.com]

In our last two posts we’ve reviewed the use of models and metaphors in designing search, and explored one particular metaphor that was valuable for both its simplicity and utility. We’ve also reviewed the different ways in which navigational context may be propagated from one stage of the search journey to the next. In this post, we provide a generic framework for understanding how those transitions guide and shape the search experience with the aid of a simple but effective spreadsheet template.

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Image by PublicCo via Pixabay [CC0 Creative Commons]

In our last post, we looked at the role of metaphors and models in search, and explored one particular metaphor that was valuable for both its simplicity and utility: the chess metaphor. This simple notion helps us frame and structure the search experience in a way that allows us to better understand the stages involved, and how they combine to form a coherent information journey.  In this post, we take a closer look the principles that govern how to propagate the user’s navigational context from one phase to the next, and how those transitions shape the search experience.

At this point you may be thinking: ‘But what navigational context is there, apart from keywords?’ Of course, for many simple (aka web) search experiences that’s all there is: a handful of keywords in the opening game that are then echoed in the middle game. But many professional search applications (such as those used by lawyers, scientists, information professionals, etc.) make a virtue of offering a relatively complex opening game in which the user is invited to articulate the full extent of their information need in the form of a complex, pre-coordinated query. In these cases, the full detail of that navigational context needs to be propagated to the middle game in a manner that makes its presence transparent and its effects easily editable.

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