Posts Tagged ‘KAPS Group’

Enterprise Search Doesn’t Have To Stink

reamyimgaeThere’s one thing that Tom Reamy, chief knowledge architect at KAPS Group, says is a continual refrain among enterprise business users: Search sucks. IT regularly attempts to make things better by buying new search engines and for awhile, everything’s good – until content grows and things start to go downhill again.

Enterprise search, he explained to an audience at this week’s Enterprise Search & Discovery summit, “is never going to be solved by search engine technology” alone. It needs a helping hand from a number of different corners to improve the experience. Good governance and taxonomies can help, for example. But there are challenges in their use, such as the fact that the people who write documents for enterprise repositories can be very creative at avoiding tasks they don’t consider their jobs, such as categorizing documents for others to find during their searches, and even if they’re willing to do it, figuring out what a document is about is a very complex decision.

And, as beautiful a structure as a taxonomy may be to behold, marrying it to millions of documents is itself complex in scale and purpose for both authors and librarians who may have had nothing to do with its creation and so can’t be counted on to apply it well.

Less recognized for the role it can play in rescuing enterprise search is text analytics.

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Insights from the SemTechBiz Conference – UK

The Semantic Technology and Business Conference – UK took place in London last week at the Millennium Goucester Hotel, and a number of themes emerged from the two-day event. A few of the sessions are highlighted below, but first, let us turn to some of the attendees to share some of their favorite insights and takeaways:

Public Sector Semantics

Professor Nigel ShadboltThere was a lot of interest in the Public Sector work. One of the presentations that highlighted the Open Data movement was Nigel Shadbolt‘s Keynote presentation about the recently launched Open Data Institute. We have covered the ODI here, and Professor Shadbolt shared some exciting insights and perspectives on the Open Data economy. In his presentation, he referred to a report on which he collaborated that was published by Deloitte Analytics. This free white paper is available for download.

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Beyond Sentiment

[Editor's Note: This guest post is by Tom Reamy, Chief Knowledge Architect and founder of KAPS Group, a group of knowledge architecture, taxonomy, and eLearning consultants. Tom has 20 years of experience in information architecture, intranet management and consulting, and education and training software.  Tom will be presenting a tutorial, Text Analytics for Semantic Applications and moderating a panel, Emotional Semantics - Beyond Sentiment at the upcoming SemTechBiz Conference in San Francisco.]

photo of Tom ReamyWhile sentiment analysis continues to generate a lot of press, it is not clear how much real value organizations are deriving from it.  One reason for that is that the standard approach to sentiment has been mostly statistical and/or long lists of sentiment terms.  However, if you add in other, advanced text analytics capabilities such as auto-categorization using advanced operators, you can not only develop more sophisticated sentiment analysis, you can also develop a whole new class of applications that either enhance and/or go beyond simple sentiment analysis.

These advanced operators include such commands as DEST_6 (count two words as a positive indicator only if they are with 6 words of each other) or SENT (only count words in the same sentence).

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Tom Reamy Introduces Enterprise Content Categorization

Chief Knowledge Architect of the KAPS Group, Tom Reamy has created a new term: enterprise content categorization, or ECC. ECC “is an element of the text analytics field (along with text mining, ontologies, sentiment analysis)” with the mission of revealing the semantic infrastructure of an organization.

According to Reamy, the core capabilities and features of ECC are categorization, entity extraction, fact and event extraction, summarization, and clustering. Taxonomy, controlled vocabulary, metadata and ontologies also fit in as “related semantic elements.” Read more