adding documentation

Docker/README.md

@@ -9,7 +9,7 @@
 - Create a virtual environment for Python 2.7 ```$ virtualenv pythenv --python=python2  ```
 	- Run the virtual environment ```$ source pythenv/bin/activate```
 	- Install the dependencies ```$ pip install elasticsearch SPARQLWrapper simplejson gensim pandas flask nltk ```
-		```pip install -U scikit-learn```
+		```$ pip install -U scikit-learn```
 - Run ```$ python IndexSDAKG.py ``` to import all the entities and labels of the KG to ElasticSearch (This is needed only when the KG is being updated or it is the first time).
 - Download [wiki-en.bin fasttext](https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.en.zip)
 	- Move the wiki.en.bin file into the data folder of the project.
@@ -26,7 +26,7 @@ $ docker run -d --name qaunibonn_fuseki -p 3030:3030 -e ADMIN_PASSWORD=robot -v
 ```
 
 ## Start ElasticSearch Server Docker
-- Pull Docker Fuseki Server 
+- Pull Docker ElasticSearch Server
 ```sh
 $ docker pull docker.elastic.co/elasticsearch/elasticsearch:6.3.2
 ```
@@ -35,7 +35,7 @@ $ docker pull docker.elastic.co/elasticsearch/elasticsearch:6.3.2
 $ docker run -d  --name elasticsearch -e transport.host=0.0.0.0 -e cluster.name=elasticsearch -e http.host=0.0.0.0 -e xpack.security.enabled=false -it docker.elastic.co/elasticsearch/elasticsearch:6.3.2
 ```
 
-If server does not connect with the container of ElasticSearch
+If server does not connect with the container of ElasticSearch Server
 ```sh
 $ docker run -d --name qaunibonn_elasticsearch -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:6.3.2
 ```

Documentation/CompleteDocumentation.md

@@ -0,0 +1,78 @@
+# Documentation of QAUniBonn
+
+## List of dependencies
+- Docker
+
+## Steps of instalation
+1. Download or clone the Git repository
+```sh
+$ git clone https://github.com/jtrillos/QAUniBonn.git
+```
+
+### Fuseki Server
+2. Pull Docker Fuseki Server
+```sh
+$ docker pull stain/jena-fuseki
+```
+2. a. Run Docker Fuseki Server
+```sh
+$ docker run -d --name qaunibonn_fuseki -p 3030:3030 -e ADMIN_PASSWORD=robot -v /path/to/Docker/Fuseki_TripleStore/:/fuseki/ -it stain/jena-fuseki
+```
+2. b. Download and extract [Apache Jena Fuseki](http://archive.apache.org/dist/jena/binaries/apache-jena-fuseki-3.4.0.zip)
+2. c. Go to apache-jena-fuseki-3.4.0/bin and run the command s-put
+```sh
+$ ./s-put http://localhost:3030/kommunikationroboter/data http://sda.tech/ path/to/Docker/Fuseki_TripleStore/backups/sda.tech.ttl
+```
+This process add the Knowledge Graph of SDA in a graph "http://sda.tech/" inside the TripleStore "kommunikationroboter".
+
+### ElasticSearch
+3. Pull Docker ElasticSearch
+```sh
+$ docker pull docker.elastic.co/elasticsearch/elasticsearch:6.3.2
+```
+3. a. Run Docker ElasticSearch
+```sh
+$ docker run -d --name qaunibonn_elasticsearch -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:6.3.2
+```
+
+### QAUniBonn Repository
+4. Install python 2.7 and virtualenv
+4. a. Create a virtual environment 
+```sh
+$ virtualenv pythenv --python=python2
+```
+4. b. Run the virtual environment
+```sh
+$ source pythenv/bin/activate
+```
+4. c. Install the dependencies
+```sh
+$ pip install elasticsearch SPARQLWrapper simplejson gensim pandas flask nltk 
+```
+```sh
+$ pip install -U scikit-learn
+```
+5. Download [wiki-en.bin fasttext](https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.en.zip)
+5. a. Move the wiki-en.bin file into the "data" forlder of the project
+6. If there is no data of NLTK in the server, uncomment the lines 8 - 11 in nerQuestion.py
+7. Run Client/IndexSDAKG python file to import all the entities and labels of the Knowledge Graph to ElasticSearch (This process is done once or when the Knowledge Graph has been updated).
+```sh
+$ python IndexSDAKG.py
+```
+
+## Run the server
+- To run the server
+```sh
+$ python app.py
+```
+- To run the server in background
+```sh
+$ nohup python app.py &
+```
+## Example of API:
+```sh
+$ curl -i -H "Content-Type: application/json" -X POST -d '{"question":"Where is SDA?"}' http://localhost:8000/ask
+```
+
+### For more information of API
+Go to the Documentation folder.

README.md

@@ -16,14 +16,12 @@
 - Create a virtual environment for Python 2.7 ```$ virtualenv pythenv --python=python2  ```
 	- Run the virtual environment ```$ source pythenv/bin/activate```
 	- Install the dependencies ```$ pip install elasticsearch SPARQLWrapper simplejson gensim pandas flask nltk ```
-		```pip install -U scikit-learn```
+		```$ pip install -U scikit-learn```
 - Run ```$ python IndexSDAKG.py ``` to import all the entities and labels of the KG to ElasticSearch (This is needed only when the KG is being updated or it is the first time).
 - Download [wiki-en.bin fasttext](https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.en.zip)
 	- Move the wiki.en.bin file into the data folder of the project.
 - If there is not data of nltk uncomment the lines 8 - 11 in nerQuestion.py
 
-If there is no data of nltk uncomment the lines 8 - 11 in nerQuestion.py
-
 ## Run Server:
 To run the server
 ```sh