Update README.md

First of all, two code files must be executed: Comparer.py and Readhumans.py. The Comparer.py requires the concept-net-assertions-5.6.0 and the selected dataset. The output is a concept-filtered and lemmatized set of words from each input set. When executing the Comparer code, the concrete path where the dataset is located must be specified. After that ontology_create has to be executed for the list with the concepts. Then the sets are split and lemmatized. Run through matching_dicts for final result with concepts. <br> Second, the Readhumans.py file is executed. As training set we use the ROCStories dev-set. The output is a file with the individual components of the set (e.g. file name). To choose the right directory in line 338 the file has to be edited specificly (dev -> motiv -> allcharlines).

<br> <br>

Graphpath: Path to constructed Concepts concept_graph_full <br>

Outputpath: Path to (empty) File, purpose written behind as _[purpose]

Purpose: --dev | --train | --test

> Graphpath: Path to constructed Concepts concept_graph_full <br>

> Outputpath: Path to (empty) File, purpose written behind as _[purpose]

> Purpose: --dev | --train | --test

### Start coding

To work, we use the steps provided by Debjit Paul from Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs (NAACL 2019)'. However, we had to modify and adapt these steps, which is why our given steps are slightly different from his. <br>

```

python src/graph_model/conceptnet2graph.py /Ppath_to_unziped_conceptnet-assertions-5.6.0.csv_File

```

Output is the file concept_graph_full

> Output is the file concept_graph_full

### Construct subgraph per sentence for for train- test files

In this step we construct the subgraph for each sentence. <br>

Input: ConceptNet Graph, Data for train/test <br>

Output: directory with files for each sentence containing subgraphs

> Input: ConceptNet Graph, Data for train/test <br>

> Output: directory with files for each sentence containing subgraphs

```

python src/graph_model/make_sub_graph_server.py "inputfile" "graphpath" "outputpath" "--purpose" purpose <br>

### Extracting relevant knowledge paths from subgraphs

Inputpath: path to inputfile from step 2 <br>

Outputpath: path to txt.file where extracted knowledgepaths can be saved <br>

Input: subgraphs and inputfile from step 2<br>

Output: .txtfile with knowledgepaths for each sentence

> Inputpath: path to inputfile from step 2 <br>

> Outputpath: path to txt.file where extracted knowledgepaths can be saved <br>

> Input: subgraphs and inputfile from step 2<br>

> Output: .txtfile with knowledgepaths for each sentence

```

python src/graph_model/extract_path3.py