Weaving Data Into Texts: The Value of Semantic Annotation

Semantic annotation is about weaving data into textual sources. In semantically annotated texts, certain words (denoting things, people, locations, organizations, etc) are linked to data – that is, to context and references that can be processed by an algorithm.

The goal of semantic annotation is better information retrieval and smarter knowledge management. Click To TweetIn particular, this translates into technologies that help content creators and consumers to retrieve information faster and manage knowledge easier. Semantic search, content aggregation, and automated relationships discovery are among the most common applications, enabled by semantic annotation.

With data woven into texts, the “new readers” (meet them in the next paragraph) are able to interpret, combine, and use content in an automated way thus facilitating the way we navigate, find, collect and analyze information.

The definition of reading does expand every single day, following our growing need to manage more and more textual sources. So does the profile of the reader. Reading, in its very basic form (extracting information from any encoded system and comprehending meaning), is not a human-only territory anymore.

Take the reading on the Web, for example. According to a recent report, although humans are the ones responsible for 51.5 % of the traffic on the web, a significant 48.5 % of all online traffic is attributed to bots. Assisting with automated tasks, machines are everywhere, not only on the web, collecting data, but also across corporate intranets.

Come to think of it, in an ocean of digital content, reading and understanding heavily depend on using the right tools for handling texts. Tools that allow efficient research, quick information retrieval and facts discovery, gathering and managing information. Activities are unthinkable without the help of software agents. These agents have huge processing powers to navigate, process and manage huge volumes of content on our behalf, provided we show them around our content and help them make sense of it. For that to happen, we need to enrich texts with information presented in the formal language the new readers understand – that is, in the language of data.

To get the benefit of understanding semantic annotation without the burden of the complexity it involves, it will help to view it as digital Marginalia.

Marginalia, the medieval side notes, have served understanding for ages and have been an invaluable source of additional information to the reader. Just like semantic annotations are today, in our digital-everything age.  Only that today’s readers are not only human.

It is through semantic annotations that we can leave notes for smart agents to process and further assist us in managing our digital content. Written in the machine-interpretable formal language of data, these notes will serve computers to classify, link, search through and filter texts and data, associated with them.