Seth Grimes recently set the record straight regarding the terms “text analytics” and “semantic content enrichment.” Grimes starts with a few definitions of text analytics: “Text analytics is a set of software and transformational steps that discover business value in ‘unstructured’ text. (Analytics in general is a process, not just algorithms and software.) The aim is to improve automated text processing, whether for search, classification, data and opinion extraction, business intelligence or other purposes.” He adds, “Text analytics draws on data mining and visualization and also on natural-language processing (NLP). Supplement NLP with technologies that recognize patterns and extract information from images, audio, video and composites and you have content analytics.”

Grimes goes on, “The concept of content enrichment is easy to grasp: Every link in this article — Web links are accomplished via the HTML ‘a’ anchor tag — is a bit of content enrichment. And semantic content enrichment? Marie Wallace puts it this way, focusing on text but with concepts that extend to the broad set of content types: ‘When I think about semantic enrichment, I see it as transforming a piece of content into a linked data source. In order to do this you do indeed need text analytics for entity and relationship extraction, but you need more than that…. A text analytics engine might recognize that [Marie Wallace] is a person, [Ireland] is a place, and Marie comes from Ireland and annotate the entities/relationships found. However when doing semantic enrichment, I would want to convert those annotations to openly addressable URIs that contribute to the linked data cloud.’”

Read more here.

Image: Courtesy Flickr/ Rubber Dragon