Month: March 2008

Enterprise Search Adopters

May-be it is this everlasting winter of weather events, but I’m ready for some big changes across the gray landscape. Experiencing endless winter has for me become a metaphor for what I observe within some enterprises as serial adoptions of search.
As I work on my forthcoming report, Enterprise Search Markets and Applications: Capitalizing on Emerging Demand, I am interviewing people who are deeply engaged in search technologies. They are presenting a view of search deployment and implementation that reinforces my own observations, complete with benefits and disappointments. However, search in enterprises is like recurring weather events, some big, some small but relentless in the repetitiveness of certain experiences. It seems that early adopters in the early stages of adoption often experience the euphoria of a fresh way to find stuff. Then inertia sets in as some large subset of adopters settles in to becoming routine but faithful users. The rest are like me with winter, looking for a really big change and more; the nitpicking begins as users cast their eyes to better options hyped by the media or by compatriots in other organizations with newer “bells and whistles.” Ah, what fickle beasts we are, as my husband will be very quick to remind me the first hot, humid day of summer when I complain in a desultory sulk.
So, I was delighted to read this article in the New York Times, Tech’s Late Adopters Prefer the Tried and True, by Miguel Helft, on March 12. I particularly loved this comment from the article: “Laggards have a bad rap, but they are crucial in pacing the nature of change, said Paul Saffo, a technology forecaster in Silicon Valley. Innovation requires the push of early adopters and the pull of laypeople asking whether something really works. If this was a world in which only early adopters got to choose, we’d all be using CB radios and quadraphonic stereo.” It helps to put one’s quest for the next big thing into perspective.
It included another quote from David Gans who, from the community of the Well in which people communicate using text-only systems, “Just because you have a nuclear-powered thing that can dry your clothes in five minutes doesn’t mean there isn’t value to hanging your clothes in the backyard and talking to your neighbors while doing it.” As one who has never owned a clothes drier, this validated one of my own conscious decisions.
Seriously though, given all the comments collected from my interviews and my own experiences, it is really time to remind adopters, early and late, to give thought to appropriateness, what benefits us or adversely distracts us in the technologies we implement in our working worlds. (I’ll leave your personal technology use for you to sort out.) Taking time to think about your intentions and “what comes next” after getting that “must have” new search system is something only you can control. Nobody on the selling side of a bakery will ever remind you that you don’t really neeeed another cookie.
And in one more point, if you are in the market for search+, Steve Arnold does a fine job of positioning the appropriateness of each of the 24 systems he reviews in Beyond Search. It might just help you resist the superfluous and take a look some other options instead.

Ontologies and Semantic Search

Recent studies describe the negative effect of media including video, television and on-line content on attention spans and even comprehension. One such study suggests that the piling on of content accrued from multiple sources throughout our work and leisure hours has saturated us to the point of making us information filterers more than information “comprehenders”. Hold that thought while I present a second one.

Last week’s blog entry reflected on intellectual property (IP) and knowledge assets and the value of taxonomies as aids to organizing and finding these valued resources. The idea of making search engines better or more precise in finding relevant content is edging into our enterprises through semantic technologies. These are search tools that are better at finding concepts, synonymous terms, and similar or related topics when we execute a search. You’ll find an in depth discussion of some of these in the forthcoming publication, Beyond Search by Steve Arnold. However, semantic search requires more sophisticated concept maps than taxonomy. It requires ontology, rich representations of a web of concepts complete with all types of term relationships.
My first comment about a trend toward just browsing and filtering content for relevance to our work, and the second one about the idea of assembling semantically relevant content for better search precision are two sides of a business problem that hundreds of entrepreneurs are grappling with, semantic technologies.

Two weeks ago, I helped to moderate a meeting on the subject, entitled Semantic Web – Ripe for Commercialization? While the assumed audience was to be a broad business group of VCs, financiers, legal and business management professionals, it turned out to have a lot of technology types. They had some pretty heavy questions and comments about how search engines handle inference and its methods for extracting meaning from content. Semantic search engines need to understand both the query and the target content to retrieve contextually relevant content.

Keynote speakers and some of the panelists introduced the concept of ontologies as being an essential backbone to semantic search. From that came a lot of discussion about how and where these ontologies originate, how and who vets them for authoritativeness, and how their development in under-funded subject areas will occur. There were no clear answers.

Here I want to give a quick definition for ontology. It is a concept map of terminology which, when richly populated, reflects all the possible semantic relationships that might be inferred from different ways that terms are assembled in human language. A subject specific ontology is more easily understood in a graphical representation. Ontologies also help to inform semantic search engines by contributing to an automated deconstruction of a query (making sense out of what the searcher wants to know) and automated deconstruction of the content to be indexed and searched. Good semantic search, therefore, depends on excellent ontologies.

To see a very simple example of an ontology related to “roadway”, check out this image. Keep in mind that before you aspire to implementing a semantic search engine in your enterprise, you want to be sure that there is a trusted ontology somewhere in the mix of tools to help the search engine retrieve results relevant to your unique audience.

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