Top 5 Semantic Technology Trends to Look for in 2017

January 17, 2017 5 mins. read Milena Yankova

Most people associate artificial intelligence with Azimov’s robots or movies and TV series like A.I.,Terminator or Westworld. AI, however, has long grown out of the imaginary sci-fi realm and has become an umbrella term for existing technologies that give machines the capacity to learn and reason.

Today’s innovative minds and advanced computer science are constantly developing technologies that ‘teach’ computers how to make links between concepts the way humans do.

To help machines understand the meaning of concepts, which enables businesses to gain a competitive advantage by turning raw data into knowledge, Ontotext has been developing and offering Semantic Technology for years.

Now that we’re rolling into 2017, we’ve identified these top 5 trends in which Semantic Technology helps enterprises make sense out of data and fine-tune offerings to customers.

Machine Learning

Machine learning will continue to drive innovation with algorithms that help computers learn and refine responses and constantly adapt to new data and content input. Machine learning models, together with semantic enrichment, are capable of classifying and adapting content so that it can be easily reused and repackaged.

This approach enables publishers to offer highly-relevant personalized adaptive content to engage, retain and win over readers, educators and news consumers. For years, the BBC has been using our Dynamic Semantic Publishing architecture to enable navigation led by concepts that are important to users, while at the same time, this semantically-enriched approach reduces production costs via content re-use and re-purposing.

We also expect that industries will continue to use machine learning for anticipating customer and user behavior, and apply predictive analytics to flag potential risks and detect and prevent fraud, in the financial services industry, for example.

While machine learning helps organizations create sophisticated products and services to sell to clients, or predict customer behavior, the second trend we’ve identified is primarily helping organizations gain a full view of all their internal records and data and make faster knowledge-backed business decisions. Here’s our trend number two.

Machine Learning

Knowledge Representation of Smarter Data

There is growing awareness among enterprises that they need to integrate all their data from various sources in various formats, in order to have all the information they need to make smart business decisions.

Organizations will continue to look for solutions that would enable them to reveal all the relationships between concepts, thus enabling their data to be ‘smart’: classified in a meaningful way and linked to other relevant datasets. Again, the technology to do this is not just in sci-fi books. Semantic data integration exists and will be evolving.

Our semantic graph database, GraphDB, brings together structured and unstructured data and creates a unified data layer as Linked Data. Data stored in atomic facts makes it easier for organizations to further classify, combine, integrate and reuse their data.

This approach to storing data enables businesses to see new links that had been implied in their data because the semantic repository is capable of inferring new knowledge out of existing facts. The new knowledge revealed is the key for businesses to obtain an all-around view of proprietary data and Linked Open Data.

Knowledge Representation of Smarter Data

Natural Language Processing

We see NLP as another winner of this year’s Semtech trends, with continuous improvement in named entity recognition, disambiguation, spelling variations and synonyms.

Thanks to its ability to break down the occurrence of terms in a sentence and to store and create relationships in a graph database, NLP helps to improve key terms in healthcare and pharmaceutical records.

NLP will continue to be a trend with businesses analyzing consumer behavior and experience as well as sentiment toward products and services.

NLP is also the basis of the next trend we’ve identified and see as further developing this year.

Natural Language Processing

Text Mining & Text Analytics

Businesses will be increasingly searching for tools to see and discover all the information in large texts. These usually contain highly valuable knowledge that is often difficult to access or organize due to its unstructured and heterogeneous nature.

Text mining makes information extraction from huge volumes of data easier and structures it as important facts, key terms or persons. Once structured data is extracted from free-flowing texts, it enables organizations to achieve efficient indexing and improved search, or offer personalized recommendations to customers.

Check out our best practices in text mining and see how our Twitter analysis on Brexit more than a month before the referendum accurately predicted the outcome when opinion polls were mostly suggesting that #Remain would prevail.

Text Mining

Internet of Things

As the IoT will continue to grow in the increasingly interconnected world with smart devices, smart cars and smart homes, we also expect more cities to become smarter by opening up their data. Open Data, apart from increasing transparency, spurs innovation, creates new business models, and engages individuals and companies to take part in app development and social projects.

As you can see, semantic technologies and Linked Open Data are not just something taken out of sci-fi literature and movies. The tech has been around for years, it has been developing, and it is here to stay and enable organizations to make sense out of data.

Internet of Things


Article's content

A bright lady with a PhD in Computer Science, Milena's path started in the role of a developer, passed through project and quickly led her to product management. For her a constant source of miracles is how technology supports and alters our behaviour, engagement and social connections.

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