Loading README.md +9 −8 Original line number Diff line number Diff line Loading @@ -53,11 +53,12 @@ To work, we use the steps provided by Debjit Paul from Ranking and Selecting Mul ### *Step 1:* Construct ConceptNet into a graph Prerequisite for this step is the previous download of the concept-net-assertions-5.6.0.csz.gz. > Inputfile: path to unziped conceptnet-assertions-5.6.0.csv > **Input** >> Inputfile: path to unziped conceptnet-assertions-5.6.0.csv > > Purpose: --dev | --train | --test > **Purpose:** --dev | --train | --test > > Output is the file concept_graph_full > **Output** is the file concept_graph_full ```python python src/graph_model/conceptnet2graph.py /"path_to_unziped_conceptnet-assertions-5.6.0.csv_File" ``` Loading @@ -66,15 +67,15 @@ python src/graph_model/conceptnet2graph.py /"path_to_unziped_conceptnet-assertio ### *Step 2:* Construct subgraph per sentence for for train- test files In this step we construct the subgraph for each sentence. <br> > Input: > **Input:** >> Inputfile: Path to train or test data <br> >> Graphpath path to ConceptNet Graph > > Output: > **Output:** >> Outputpath: path to folder where output will be saved <br> >> Output: directory with files for each sentence containing subgraphs > > Purpose: --dev | --train | --test > **Purpose:** --dev | --train | --test ```python python src/graph_model/make_sub_graph_server.py "inputfile" "graphpath" "outputpath" "--purpose" purpose Loading @@ -89,11 +90,11 @@ Seperate sentences with newlines. ### *Step 3:* Extracting relevant knowledge paths from subgraphs > Input > **Input** >> Inputpath: path to inputfile from step 2 <br> >> Input: subgraphs and inputfile from output of step 2 <br> > > Output > **Output** >> Outputpath: path to txt.file where extracted knowledgepaths can be saved <br> >> Output: .txtfile with knowledgepaths for each sentence Loading Loading
README.md +9 −8 Original line number Diff line number Diff line Loading @@ -53,11 +53,12 @@ To work, we use the steps provided by Debjit Paul from Ranking and Selecting Mul ### *Step 1:* Construct ConceptNet into a graph Prerequisite for this step is the previous download of the concept-net-assertions-5.6.0.csz.gz. > Inputfile: path to unziped conceptnet-assertions-5.6.0.csv > **Input** >> Inputfile: path to unziped conceptnet-assertions-5.6.0.csv > > Purpose: --dev | --train | --test > **Purpose:** --dev | --train | --test > > Output is the file concept_graph_full > **Output** is the file concept_graph_full ```python python src/graph_model/conceptnet2graph.py /"path_to_unziped_conceptnet-assertions-5.6.0.csv_File" ``` Loading @@ -66,15 +67,15 @@ python src/graph_model/conceptnet2graph.py /"path_to_unziped_conceptnet-assertio ### *Step 2:* Construct subgraph per sentence for for train- test files In this step we construct the subgraph for each sentence. <br> > Input: > **Input:** >> Inputfile: Path to train or test data <br> >> Graphpath path to ConceptNet Graph > > Output: > **Output:** >> Outputpath: path to folder where output will be saved <br> >> Output: directory with files for each sentence containing subgraphs > > Purpose: --dev | --train | --test > **Purpose:** --dev | --train | --test ```python python src/graph_model/make_sub_graph_server.py "inputfile" "graphpath" "outputpath" "--purpose" purpose Loading @@ -89,11 +90,11 @@ Seperate sentences with newlines. ### *Step 3:* Extracting relevant knowledge paths from subgraphs > Input > **Input** >> Inputpath: path to inputfile from step 2 <br> >> Input: subgraphs and inputfile from output of step 2 <br> > > Output > **Output** >> Outputpath: path to txt.file where extracted knowledgepaths can be saved <br> >> Output: .txtfile with knowledgepaths for each sentence Loading
