Page 192 - DLIS402_INFORMATION_ANALYSIS_AND_REPACKAGING

Unit 10: Pre-coordinate, Post-coordinate and Citation Indexing

“ ‘Freezing’ didn’t work. What else can I try?” And it is at that moment of “What else can I try” that Notes
a user loves to see a list of other categories, or terms, or an analyzed index.
Unless a browsing search presents categories or analysis, information may become unretrievable
for the user who is at a loss for words. Categories can be provided by indexes and other such
analyzed lists of content in context. There is a browsing period in the search process that natural
language engines don’t accommodate, a time when a reader wants to know what other types, what
other modes, what other features, what other subsets, or what other ideas he or she can use to solve
a problem or get more information.
In this light, who is right in predicting the trends in indexing? I said all three. Seth Maislin is right,
because the people who tend to do indexing, whether freelancers or in-house, are going to see the
need for their categorization, classification, and language fine-tuning as companies face the fact
that they have libraries and libraries of bits of information to control and make retrievable.
Microsoft is right about their analysis of their own indexes, because they have not exposed true
indexes to their users in their mainstream products for several years, so indeed, “no one” is using
the index in their products. Their users have learned not to expect much from that Index tab. And
Apple is right to make a form of indexing accessible again, because they recognize that users take
alternating paths to information. Users have different learning styles, different searching styles,
and different iterating paths within one search session.
As Gordon Meyer of Apple says, “The Apple Help search engine is really quite good (full-text,
natural language) but some users just aren’t ‘searchers.’ The index is there to provide alternative
access for those who don’t, or won’t, use the search function. A key reason behind its inclusion
now, as opposed to in an earlier version is that we’ve added a technical solution for generating the
index ‘on-the-fly’—based on tagging done by instructional designers—which makes interlinked
pages much more compatible with our continuous publishing, Internet-driven, model.”
Serving all your users, and all your information, may mean using an old form of access and linking
it to information you haven’t even written yet. Predicting what topics to interlink to an index means
categorizing and classifying the nature of the knowledge your company publishes now, and is
likely to publish in the future.

It’s About Aboutness

There is still a strong need to connect “aboutness metadata” to chunks of content. That aboutness
metadata can be exposed, as in an index, or listed in a categories list, or hidden in fields and used by
a fine-tuned search engine. Indexes may go away in the next version of Longhorn, but they will be
back in other ways, because it still takes human analysis to provide oversight on “aboutness.” Searching
for content in the right context is a last frontier, and although we are on the edges of the frontier, we
still don’t have automated content retrieval completely solved. We get a lot of results that don’t meet
our needs at the time, or when we switch search modes. There’s still a lot of unfindable information.
Aboutness metadata provides the contextual clues.
In the last several conferences I’ve attended, an emphasis has been put on metadata schemas. One
company I’ve contracted with has over 100 fields of metadata to be filled out on each document for
its intranet. I don’t think attaching that much metadata to content will solve all the issues, because
employees don’t have that much time, and companies normally don’t have that much money.

Automation or natural language engines will probably not solve it all, because if they
did, both Google and the talking paper clip would always work.
For the most part, content developers do not want to take the time to keyword documents and fill in
metadata fields. When this individual reluctance scales up to a large help system, you are left with

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