I don’t normally attempt to predict the future. If anything, the future is more fluid than either the past and/or the present.
On the other hand, we judge predictions from the vantage point of some future time. If our predictions are vague enough, it is hard to be considered wrong. 😉
I will try to avoid the escape hatch of vagueness but you will have to be the judge of my success. I am too close to the author to be considered an unbiased judge.
My first prediction is that Google’s Hummingbird (How semantic search is killing the keyword) which is a marriage of very coarse annotations (schema.org) to Google’s Knowledge Graph, will demonstrate immediate ROI for low cost semantic annotation.
The ROI that the Semantic Web of the W3C never demonstrated.
Semantic Web ROI awaits a pie in the sky day when all identifiers are replaced by URIs, URIs used consistently by everyone, written to enable machine reasoning, at each author’s expense.
Because of that demonstration of ROI from annotation coupled with the knowledge graph and the Google search engine, my second prediction is that a hue and cry will go out for more simple annotations in along the lines of those found at schema.org.
Commercial, government and NGOs, that supported and waited for the Semantic Web for fifteen (15) years, with so little to show for it, will not be as patient this time.
They will want (demand) the same ROI as Google. Immediately if not sooner, not someday by and by.
The coarse annotations invented by governments, organizations, commercial interests and others will be inconsistent and often contradictory. Not to mention it is hard to apply annotations to data you don’t understand.
You and I recognize the semantic opaqueness of keys and values in unfamiliar data. It goes unnoticed by someone familiar with a data set, much in the same way you can’t look at a page and not read it. (Assuming you know the language.)
Data and their structures are much the same way. We can’t look at data we know (or think we do) and not understand what is meant by the data and its structure.
But the opposite is true for data that is foreign to us. Foreign data is semantically opaque to a visitor.
There is a lot of foreign data in big data.
Enough foreign data that my third prediction is that “Big Dark Data” will be one of the major themes of 2014.
I see topic maps (both theory and practice) as an answer for Big Dark Data.
Do you?
Summarizing my predictions for 2014:
- Google will demonstrate ROI from the use of coarse annotations (schema.org) and its knowledge graph + search engine.
- Governments, enterprises, organizations, etc., will seek the same semantic ROI as Google.
- Big Data will become known as Big Dark Data since most of it is foreign to any given user.