conTEXT : One click text analytics thanks to Linked Data

Recently I was working on a tool for lightweight text analytics using Linked Data. It is called conTEXT since it performs analyzes with (Latin `con’) text and provides contextual visualizations for discovered entities in text. conTEXT is now publicly available at http://context.aksw.org

conTEXT workflow

The process of text analytics in conTEXT starts by collecting information from the web. conTEXT utilizes standard information access methods and protocols such as RSS/ATOM feeds, SPARQL endpoints and REST APIs as well as customized crawlers for WordPress and Blogger to build a corpus of information relevant for a certain user. The assembled text corpus is then processed by Natural Language Processing (NLP) services (currently FOX and DBpedia-Spotlight) which link unstructured information sources to the Linked Open Data cloud through DBpedia. The processed corpus is then further enriched by de-referencing the  DBpedia URIs as well as  matching with with pre-defined natural-language patterns for DBpedia predicates (BOA patterns). The processed data can also be joined with other existing corpora in a text analytics mashup. The creation of analytics mashups requires dealing with the heterogeneity of different corpora as well as the heterogeneity of different NLP services utilized for annotation. conTEXT employs NIF (NLP Interchange Format) to deal with this heterogeneity. The processed, enriched and possibly mixed results are presented to users using different views for exploration and visualization of the data. Additionally, conTEXT provides an annotation refinement user interface based on the RDFa Content Editor (RDFaCE) to enable users to revise the annotated results. User-refined annotations are sent back to the NLP services as feedback for the purpose of learning in the system.

For more information on conTEXT visit:

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