Text Analytics and Semantic Annotation

Semantic annotation is the automated and manual application of linked data identifiers to media adding significantly to the utility of applied tags. datalanguage can help you apply machine driven text analytics to semantically annotate your digital media.

Flat tagging or labelling of content with meta-tags has become common place and is used extensively on blogs and news content to aid search and contextual surfacing of content. Semantic annotation is a relatively simple step further than flat tagging but can add significantly to the utility of applied tags. Semantic annotation is the application of ontologically modelled references (linked data) to your digital media content.

Instead of simply applying a free text word or phrase as a tag, you apply (associate) to the relevant piece of content a URI reference to an instance of a "thing" that has been adequately domain modelled. As an example, if we applied as free text the term "Elvis Presley" to a music article, we would be able to some extent allow users to know that this article was about Elvis Presley, and maybe be able to explore for other articles that have been tagged with the (exact) same phrase. Although if an another article was tagged with "Elvis" there most likely be no correlation between the two terms. If, however, we associated a URI referencing "Elvis Presley" in some ontological domain model to the content, then we also gain all knowledge about Elvis from the underlying model. We can also assert that our instance of Elvis Presley is the same as that of another linked dataset (such as Freebase, MusicBrainz, or DBpedia) thus also be able to surface all the knowledge from those datasets too.

We can provide solutions for text mining, language processing, semantic disambiguation and concept extraction for semantic annotation. If you wish to semantically annotate your text content, datalanguage can help build out fully automatic or semi-automatic solutions for analysing your content for semantic concepts. We also have considerable expertise in semantic geotagging, and geospatial semantic querying. By annotating content with geospatial semantic metatags, your content can be aggregated both geospatially and semantically. Examples of this would be "Give me all the articles about car accidents within 25 kilometres of London", or "Give me all the political news within the state of New York".

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