Companies Think Semantic Thoughts
In a recent web seminar that we participated organized by Project 10X some 260 registered attendees submitted questions prior to the event. I semantically processed these questions (sometimes called “eating your own dog food” – imagine that!) looking for common themes and concerns.
In reviewing the outcome here is what I found;
1. Case Studies and ROI. People learn best with storytelling and proof points embodied by Return on Investment. So it should be no surprise that this tops the list of questions and concerns. These stories help convince funders, provide guidance for technical planning, and show feasibility. Yet this also shows a level of understanding of the technology by the participants. In other words they are convinced of the basic value parameters of semantic technologies and have come to believe they can be deployed with good outcomes within their organizations but need help to find the right place to start, the expected timelines, and how to sell the capabilities and outcomes to upper management. At Expert System we have over 100 implementations in the last 3 years alone and can confirm this concern meets with our experience.
2. Technical Integration Points. Here attendees concerns are about how to make semantics live with or interact with existing applications, data sets, and search products. Here I sense the need to make existing products pay a bit longer for their sunk cost and not to tear things out wholesale and start over. The good news is that semantic technology is intended to play this exact role by providing new insight into information where ever they currently live. 9 out of 10 customers ask us for a SAAS implementation with a front end user interface that already exists.
3. Semantic Networks. This is a real surprise to us but pleasantly so. While our technology relies heavily on a semantic network, sometimes called ontology, it is not always the case that other providers use this method to unlock the meaning of text. Some use statistical approaches, others heuristics and still others something called latent semantic processing. These other approaches tend to sound quite scientific but in reality are short cuts that prove to be less than sufficient for industry strength precision and recall. Semantic Networks are hard to produce and they take time. But the investment pays off. They become a knowledge representation of a domain of knowledge. When done thoroughly and properly can increase the precision and recall of the processing greatly. Many networks are specific to a branch of science or hold deep technical knowledge representations. Our semantic network, on the other hand, is of the common language, covering all topics, all words, all concepts and the connections between them. This means it can be applied to any domain.
4. W3C standards are confusing. When we read the comments its clear there are too many acronyms and to many standards. More concerning, the standards themselves seem to be the solution to semantics. It is as if many seem to think the standards provide the inference, the storage, the modeling, the interpretation and more that are core to semantics. The reality is that standards are only a proposed common language for describing and exchanging the outcomes of semantic processing.
To sum up – the semantic web has come a long way in terms of showing value and laying down a base of understanding. But as with any new technology, there is more to do. All of us to do better in terms of explaining, simplifying and educating up and down the organizational decision chain. Only when that is done will we be able to say “it’s baked”.
Where the categories mean the following;
Integration: How to embed or use semantics behind the scenes of existing applications.
Mobility: Get semantics to support mobile workers.
ROI Case Studies: Examples of successful, killer applications and their payback.
Semantic Nets: Semantic networks or ontologies, what they are, when to use them, how to maintain them.
Standards: W3C’s soup of acronyms and what they mean.
Timing: How fast will the technology and/or market progress.
Performance: Can semantics run with everything else and keep up.
Databases: How and when to use databases with semantics.
Automatic: Do semantic systems or tools learn on their own. What about maintenance and support.
Selling: How to make the case for funding to upper management.
NLP: how does semantics support natural language processing or computing.