Local Search – How Hard Can It Be? by Matthew Hurst.
From the post:
This week, Apple got a rude awakening with its initial foray into the world of local search and mapping. The media and user backlash to their iOS upgrade which removes Google as the maps and local search partner and replaces it with their own application (built on licensed data) demonstrates just how important the local scenario is to the mobile space.
While the pundits are reporting various (and sometimes amusing) issues with the data and the search service, it is important to remind ourselves how hard local search can be.
For example, if you search on Google for Key Arena – a major venue in Seattle located in the famous Seattle Center, you will find some severe data quality problems.
See Matthew’s post for the detail but I am mostly interesting in his final observation:
One of the ironies of local data conflation is that landmark entities (like stadia, large complex hotels, hospitals, etc.) tend to have lots of data (everyone knows about them) and lots of complexity (the Seattle Center has lots of things within it that can be confused). These factors conspire to make the most visible entities in some ways the entities more prone to problems.
Every library student is (or should be) familiar with the “reference interview.” A patron asks a question (consider this to be the search request, “Key Arena”) and a librarian uses the reference interview to further identify the information being requested.
Contrast that unfolding of the search request, which at any juncture offers different paths to different goals, with the “if you can identify it, you can find it,” approach of most search engines.
Computers have difficulty searching complex entities such as “Key Arena” successfully. Whereas starting with the same query with a librarian does not.
Doesn’t that suggest to you that “unfolding” searches may be a better model for computer searching than simple identification?
More than static facets, but a presentation of the details most likely to distinguish subjects searched for by users under similar circumstances. Dynamically.
Sounds like the sort of heuristic knowledge that topic maps could capture quite handily.