Update README.md

README.md

+# TENET - Terminology Extraction using Net Extension by (semantic) Transduction
+-------------------------------------------------------------------------------
+
+This tool exploits an intermediate semantic representation (UNL-RDF graphs) to 
+construct an ontology representations of NL sentences. [TODO: compléter]
+
+The treatment is carried out in two stages: 
+
+  1. Initialization: TODO.
+  2. UNL sentences Loading: TODO. 
+  3. Transduction Process: the UNL-RDF graphs are extended to obtain semantic nets.
+  4. Classification / Instanciation
+  5. Reasonning
+
+[TODO: compléter la description]
+
+
+## 1 - Implementation
+
+This implementation was made using Python languages, with UNL as pivot structure. 
+
+[TODO: talk about UNL-RDF graph (obtained using UNL-RDF schemas)]
+
+The following module is included as main process: 
+
+  1. Semantic Transduction Process (stp) for semantic analysis with transduction schemes
+
+The python script _tenet.py_ is used to manage the tool's commands, using components of the directory _scripts_. 
+The data to be processed must be placed in the directory _corpus_. All working data, including the results, 
+will be processed in the directory _workdata_.
+
+Transduction process configuration includes an ontology definition for semantic net, 
+and several transduction schemes as SPARQL request.
+
+
+## 2 - Environment Setup
+  
+[TODO: external module souces?]
+
+The python code has been tested on Python 3.7. 
+All dependencies are listed in requirements.txt. These dependencies are used for external modules. 
+
+The input directories contain evaluation files with some test corpus.
+
+
+## 3 - Execution
+
+The application runs in a terminal using the tenet.py script: **python3 tenet.py <command> <args>**.
+
+This prototype was tested with a standard computer configuration. The processing time is reasonable for both processing steps. 
+
+The following times were measured for the processing of a file of 10 sentences:
+
+  * about xxx seconds for initialization and UNL sentences loading;
+  * about xxx second for transduction, classification and instanciation process;
+  * about xxx second for reasonning process.
+
+
+## 4 - Commands
+
+Following commands are proposed to execute the different steps of the process:
+
+  * **select**: command to select a corpus.
+  * **load**: command to load the UNL sentences of a given corpus.
+  * **extraction**: command to extract terminologies data from UNL-RDF graph.
+  * **reason**: command to reason on terminology.
+  * **clean**: command to clean the working directories.
+
+These commands are used with the python script _tenet.py_.
+
+## 5 - Example
+
+[TODO: end-to-end example] 
+
+
+
+
+## 5 - Evaluation
+
+[TODO: experimentation]
+
+
+-------------------------------------------------------------------------------
+
+# References
+-------------------------------------------------------------------------------
+
+--
+