Teach machines how to understand and analyze texts!
TEA stands for Text Encoder and Annotator. By using TEA, you can enrich digital representations of textual documents by using a Markdown-inspired language.
The TEA language lets you define sections of interest, called spans, by surrounding text with double angle brackets:
<<Bob>> is a good guy. <<He>> is friend with <<Alice>>.
Spans can be given an alphanumeric string as an identifier:
<b<Bob>b> is a good guy. <b<He>b> is friend with <al<Alice>al>.
In most cases, the first identifier can be omitted:
<<Bob>b> is a good guy. <<He>b> is friend with <<Alice>al>.
In the special case of non-hierarchical overlap though, both identifiers are needed:
This is a case of <1<non-<2<hierarchical>1> overlap>2>.
In the above example, the first span, identified as 1, contains the text non-hierarchical, while the second one (2) contains hierarchical overlap.
When spans are defined, it is then possible to add RDF triples describing the selected portions of text by creating a special code block, delimited by lines with +++:
<<Bob>b> is a good guy. <<He>b> is friend with <<Alice>al>.
+++
al rdf:type foaf:Person
b rdf:type foaf:Person
al foaf:knows b
+++
[FIXME: change the example to use real predicates - rdf:type can't be used like that] [FIXME: these are not real triples - they are more like triple templates]
npm install grunt
npm install grunt-contrib-coffee
npm install grunt-contrib-watch
npm install grunt-contrib-concat