As I have written many times before, semantic technology is unique in that it is able to go beyond the limits of other types of technology and approach the automatic understanding of a text. It is not perfect, however, and it certainly has yet to reach its maximum potential.

I realize that it’s not that easy for those who don’t work in the sector to understand (especially due to the fact that there are so many false promises out there, which tend to create unreasonable expectations, muddled ideas and market chaos). Therefore, it might be useful to use a common experience as an example, such as: our learning process.

Let’s start from the beginning: from the moment we (human beings) begin to talk, understand, learn, go to school, etc… We require at least 12-15 years to be able to read a newspaper and understand the most general articles and this is thanks to the experience we developed while learning the meanings of words and experimenting with a great deal of different phrase constructions. Consequently, the learning process is  lengthier when we decide to tackle more technical terms or specific topics.

Learning takes time, and the same goes for a computer. It’s true that a computer can process in nanoseconds while we think in milliseconds, but it is also true that our method of learning uses a device (the brain) that no one has been able to fully understand and that is able to do things that not even the most powerful computer can imitate.

In summary, it doesn’t make sense to expect that a computer be able to perfectly analyze and understand a biology text, for example, without first having learned all it can about that subject. There are no shortcuts nor magic formulas: learning a language is difficult and even automatic processes require time and labor.


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