Read about how the more media publishers know about how users consume their content, the more relevant their content and ad recommendations will be.
Semantic Publishing includes a number of techniques including semantic recommendations used to personalize the user experience by delivering contextual content based on natural language processing, search history, user profiles and semantically enriched data.
Finding things quickly is one of the things we all like about Google. When we search using Google, we typically find what we are looking for. What if your organization had the same powerful search and discovery capabilities?
Today more than ever, companies struggle with search and discovery. Massive volumes of unstructured data contain meaning but it’s hard to find in all the noise. Policy analysts search through compliance documents. Authors search through historical content. Clinical trials researchers cull through Mount-Everest-like text relating to drugs, adverse effects, research and more. It never seems to end.
But what if you could find the exact document, the precise paragraph and the perfect reference to a specific topic instantly? Better yet, what if your website visitors could do that as well?
It’s all possible.
In fact, this is being done by some of the largest media, publishing, pharma and government organizations in the world. The application of semantic technology to accomplish this goal manifests itself in many ways. Internal users gain access to relevant content enabling them to do their job faster. External users find exactly what they need – relevant, contextual content personalized for them.
This is much more than simple content tagging. When you decompose this process and technology, what you find is that semantic recommendation engines are analyzing a lot of data. For starters, they know the web visitor profile and search history.
But operating behind the scenes is a much deeper semantic technology that extracts concepts and entities from the articles viewed. Results are stored in a high-performance triplestore for search, analysis and discovery purposes. The magic happens when the profile and search history are matched to the newly structured semantic facts and the current search criteria.
At Ontotext we call this semantic recommendations (a subset of semantic publishing) because our customers are able to instantly deliver highly relevant, recommended articles. At the same time, hundreds of queries per second are taking place on your website, authors can be enriching new content, which is committed to the database and available for the next search. Simultaneously, the text is being processed, entities are classified and the same person with different name spellings are being identified and stored.
This semantic wizardry is known as semantic annotation that has a series of techniques at its core. Semantic enrichment allows users to enrich entities with valuable information used in identity resolution and search. Semantic publishing assembles and delivers personalized web pages using a variety of unstructured data types and semantic facts about the people, places or organizations that the visitor is searching for. Semantic curation prompts authors or researchers with relevant curated content as they write. We could go on and on…
The bottom line on all of this is one word: Productivity.
Everyone wins. Researchers find content faster. Decision makers are accurately informed using a combination of real-world facts and their own data. Writers produce more content. Website visitors get recommendations they never thought were possible.