The Emergence of Semantic Information Processing
Semantic information processing is not a new concept. In fact, it has been the subject of much research and development in the past decade. However, it is only in recent years that its use has become more understood and that businesses have begun waking up to the advantages it provides. The emergence of semantic information processing is now part of the growing AI and machine learning space.
To put the idea into extremely simple terms, semantic information processing is the ability to automatically derive meaning and understanding from data. In effect, a computer or machine becomes able to “read between the lines,” utilising clues to comprehend and act upon data in a sophisticated manner similar to the human mind.
Let’s take the word “treacherous” as an example. A computing system could know, just as we do, that there is more than one definition for the term. Treacherous can mean being two-faced and untrustworthy, or it can mean dangerous and hazardous. If the system comes across the sentence “the path was treacherous because it was near the cliff edge,” it must make a decision regarding the meaning in this context. If the system is able to work this out, then it has used semantic information processing to do so. However, it is not merely a matter of keyword matching based on pre-configured keywords, it goes much deeper and wider than that.
Internet Accessibility
A way in which semantic information processing has affected our daily lives is found in the way we use the internet. Search engines were once packed with pages which utilised irrelevant keywords in an attempt to “game the system” and leverage a higher search engine position. Semantic information processing effectively puts a stop to that.
For example, a webpage designed to advertise a car cleaning service might also play host to other key phrases related to computer repair or to some other discipline. The idea here was to capture incidental traffic and to boost conversions in the process. Of course, the result of such an exercise is a whole lot of confused computer repair customers and a very displeased set of search engine executives.
To combat this, search engines used Latent Semantic Indexing which is also a form of semantic information processing. LSI uses algorithms and empirical databases to discover words associated with the title of a webpage. So, a webpage advertising a car cleaning service would include written references to cars, cleaning products, auto components, and aftercare recommendations—these words would be considered to be in the same semantic field.
Big Data and Semantic Information Processing
You could say that Big Data and semantic information processing have a bright and exciting future. After all, Big Data represents the next big application for semantic information processing, just like semantic information processing represents the key to unlock Big Data’s full potential.
Why? Well, just look at the volume of data we now have to deal with on a daily basis. The Internet of Things—or IoT—is upon us, which means more data sources and, consequently, much more data to be derived from these sources. This is certainly a positive development but it requires an element of caution. If we want to avoid drowning in data, we must have the smart systems and protocols in place to deal with it.
This is where semantic information processing can provide such a benefit. If the systems we use to carve this data up into more manageable chunks do not understand the data, we run the risk of having serious issues. With semantic information processing in place, systems can instantly recognise connections and links between fields and points of data, identifying relationships and laying the groundwork for serious advantages in data insights and understanding.
To date, many attempts to leverage big data have been predicated on an assumed personal point of view to which one or more data sets have been applied in order to validate (or otherwise) that view. With just a few data sets this bias remains in place. However, if you are able to harmonise many more data sets, the element of personal bias lessens greatly and when processed semantically, the bias virtually disappears. This is then the point of data-driven decision-making.
Turn Your Back on Semantic Information Processing at Your Peril
Do you want to be the person at the party who doesn’t get the joke, but feels impelled to laugh along anyway, possibly compounding your shame? If you don’t, then you and your organisation need to firstly understand and then harness the benefits provided by semantic information processing.
The growth in Big Data volume is not slowing down anytime soon. Every second in 2015, searchers performed 40,000 Google queries on their way to racking up 1.2 trillion searches for the year—and this is just a single data source. Projections show that accumulated data will have reached 44 zettabytes by 2020—equal to 44 trillion gigabytes—and will continue to grow.
If your organisation is to leverage Big Data for business insight and not be left behind, it needs semantic information processing. There is no middle ground here; companies which thrive will be the companies that understand the data they encounter, collect, and analyse. Semantic information processing is the key to this understanding.
Big Data interpretation platforms such as Latize Ulysses can help you apply semantic information processing to successfully gather and harness data insights, effectively putting you ahead of the competition and in a position to improve your business and take it to the next level.
