Some colleagues were asking me how I approach search recently and I found that I was repeating myself. The light bulb went off so I wrote this blog.
1. Utopian Search Model
We don’t live in Utopia so why did I include this? At first pass many people will build a search interface with the hidden assumption that they are using the Utopian Search Model – and get frustrated when search doesn’t work. The hidden assumption that kills us is that we assume that a user can enter the exact keywords needed to retrieve the specific documents they need to find every time. Search works “auto-magically.”
The Utopian Search Model can have accidental success. Rarely does a user know how to enter the right keywords to map exactly to the documents required.
2. Item Search Model
An effective user interface supporting Item Search Model provides a map between what the user knows and how the system describes the objects. Examples include searching for articles, books, and specific information objects. Often user knows enough information to uniquely identify the desired object. This may seem very similar to the Utopian Search Model but there are key differences. The main difference is that while a user may know specific information about a document – but the information might not be unique to that document. For example, there may be different versions of the same document or different documents by the same title or author.
System has uniquely identified specific item either through a single field or a matrix of fields
User has knowledge of how to submit a description that sufficiently narrows the result set so the specific item can be identified.
3. Untagged or Hidden Information Search Model
This model works when people search for information without knowing whether it is actually exists or not and without knowing for certain what form it might take. The effective search tool in this space provides the user with a means for “chunking information” in meaningful ways and subsequent browsing.
It is an iterative process
This is NOT the model followed with Google except at the most coarse of levels.
Occurs when a information repository system does not uniquely/adequately identify “knowledge” or “information” or the end-user is unable to derive the right matrix of attributes and corresponding descriptions to retrieve desired information
A Methodology for Locating “Hidden” information
Basic problems faced by people seeking “hidden information:”
How to find information not explicitly/adequately described by system?
How to effectively describe what is only partially understood or seen?
A user needs to divide information into meaningful chunks and explore the resulting “Klondike Space” to see if desired info is available in that space.
The phrase “Klondike Space” comes from the book The Eureka Effect. In the book the phrase evokes the image of a prospector looking for gold. The gist is that a prospector could look at topographical clues and identify places more likely to contain gold than others. Some prospectors were good; many were not. Here I use the phrase to compare a person searching with the prospector and the full set of documents as the area being mined. A good searcher looking for “hidden” or untagged information knows how to chunk the documents in a way that creates a set of documents where an answer is more likely to be found.
Doing a keyword search is just one way of creating a chunk of documents. Many times that works but when it doesn’t then more sophisticated ways of “chunking” needs to be employed – beyond guessing at a series of keywords. There are other ways to create potentially rich chunks that increase the likelihood of finding the information needed.
Creating ways that allow a user to meaningfully chunk information for their business needs is the goal of search architecture. Different information types often require different chunking strategies. That shouldn’t be a surprise. If you have ever went into a store that sells hats and watch people try them on, you’ll know that although the tag reads “One Size Fits All” – it doesn’t.
What’s the Next Step in the process? Identifying what metadata is required to support Item Search and Untagged Search requirements.
And the step after? Identifying what metadata is available and developing a strategy to create additional metadata (Spoiler alert: Crowd-sourcing).