As part of my PhD research I have been exploring search technology, search driven applications and the modality of use cases. The model below is a suggested continuum of use cases.
The model above encapsulates ‘classic search’ in the bottom left hand corner, providing lists of results (web pages, documents) to meet an existing need. Precision (accuracy) dominates. Internet search like Google and library search technologies would fit here. Such information needs may include ‘I need to find the Group Investment Proposal for Project Orange’, ‘I need to find the company Video Tube site’. Above it in the top left hand corner, we move from these containers of information (web pages, documents) to entities, concepts and associations, providing simple factual ‘rich’ answers to questions. These may include answers to questions such as ‘What is the Capital of Australia’, ‘What is the current exchange rate between the dollar and sterling’, ‘Where is Aberystwyth?’. In these two scenarios, search is often perceived as a time saver (although there may be another way to find that information), facts are facts, independent of the individual performing the search, there is a perceived ‘right answer’.
In the bottom right hand corner the focus is on personalization of some form (to individual, transaction or task). There is not necessarily a ‘right answer’, it is subjective, what one person may find useful and interesting, another may not. Pertinence and popularity, rather than accuracy dominates. Knowledge is constructed in the mind of the searcher, a new need may be stimulated by the search technology. Task based portals and social networks using usage analytics driving activity feeds are encapsulated in this area. In this way you may find out about an upcoming conference in your discipline that you were not aware of, because the blog notification of one of your colleagues has been pushed to you via your activity feed.
Above this, in the top right hand corner the focus is on concepts, entities and associations but with more sophisticated knowledge engineering and statistical techniques adding context. These techniques can enable ‘search’ to suggest and recommend, to perform some form of analogical reasoning. Content analytics, although used throughout the model, is probably most important in this sector. What is unusual as an association, may be as valuable (if not more so) than what is most popular. For example, you may be interested to see geological formations anywhere in the world where an igneous intrusion sill has changes the properties of a limestone oil reservoir. A search based application could return these ranked by ‘most similar’ using the textual co-occurrence patterns around geological formation names.
This right hand side of the model supports creativity and learning, rather than just time saving. The bimodal nature of search.
Information Architecture (IA) and Information Management practices underpin this modality, without quality in these areas, any resulting search outcome is likely to be sub-optimal. This technological and architectural framework is enabled through a socio-organizational ‘soup’ of sub-cultures and competence. The role of information literacy and sensemaking of staff, may be just as crucial as the technology when assessing enterprise search and discovery capability.