Add Dataset

To support a new Dataset into the Data Integration step follow these steps:

Fork the repository, pull the latest changes and create a new branch add-dataset-{dataset_name}
Create a bash script to download the raw data and store it into a new folder with the name of the dataset.
- Place it into the datasets/ folder.
Implement a Dataset subclass for your dataset.
- from ..dataset import Dataset
- Your subclass must override the following methods:
  - __init__() to instantiate the class.
  - load_item_data() for converting the item data into a pd.DataFrame().
  - load_user_data() for converting the user data into a pd.DataFrame().
  - load_rating_data() for converting the rating data into a pd.DataFrame().
  - load_social_data() for converting the social link data into a pd.DataFrame(), if supported.
  - entity_linking() for matching each item with a DBpedia resource.
  - enrich() for enriching each item with DBpedia’s properties.
- Your code must be placed in a new file at data_integration/datasets/.
Store the submodule path to dinamically load the subclass.
- Go to the data_integration/dataset2class.py file. This file store the mapping between the dataset name and the submodule path and class name.
- Create a new key with the dataset’s name. This dataset name will be used to identify the dataset when using the data_integration.py script.
- Store in this new key, the submodule: path, from data_integration/, and the class: name.
- For example:
```
dataset2class = {
    # ....
    'ml-100k': {    # Dataset name
        'submodule': 'datasets.movielens',  # submodule path
        'class': 'MovieLens100k'            # Class name
    }
}
```
Add the dataset into the documentation
- In the file docs/source/getting_started/support.md and Datasets section, add the dataset to the table.
- Inform the dataset name with reference link, number of matched items and total number of items of the dataset.
Make a Pull Request on Github.