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@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+cn_relations_clean.json filter=lfs diff=lfs merge=lfs -text

cn_relations_clean.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0efc7e1ab6d75dc7392be33765b5cbba9d8f388d321a8e0dbf51d33840800d37
+size 961527622

src/cn_analyze.py

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+import json
+
+# Load the relations dictionary from the JSON file
+input_file_path = "cn_relations_clean.json"
+with open(input_file_path, "r") as json_file:
+    relations_dict = json.load(json_file)
+
+# Prepare the results
+results = []
+num_languages = len(relations_dict)
+results.append(f"Number of languages: {num_languages}\n")
+
+for language_code, start_nodes in relations_dict.items():
+    num_start_edges = len(start_nodes.keys())
+    num_end_edges = len([end_node for edges in start_nodes.values() for end_node in edges])
+    results.append(f"Language: {language_code} - Number of start edges: {num_start_edges} - Number of end edges: {num_end_edges}\n")
+
+# Write the results to a text file
+output_file_path = "cn_relations_summary.txt"
+with open(output_file_path, "w") as txt_file:
+    txt_file.writelines(results)
+
+print(f"Summary saved to {output_file_path}")

src/cn_clean.py

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+import json 
+import re
+
+def clean_prefixes(data):
+    cleaned_data = {}
+    prefix_pattern = re.compile(r'^/c/[^/]+/')  # Matches the prefix part '/c/<lang_code>/'
+
+    for lang, words in data.items():
+        cleaned_words = {}
+        for key, values in words.items():
+            # Clean the key
+            cleaned_key = prefix_pattern.sub('', key).replace('_', ' ')
+                
+            # Clean the values
+            cleaned_values = [prefix_pattern.sub('', value).replace('_', ' ') for value in values]
+                
+            cleaned_words[cleaned_key] = cleaned_values
+        
+        cleaned_data[lang] = cleaned_words
+    
+    return cleaned_data
+
+# Load the relations dictionary from the JSON file
+input_file_path = "cn_relations.json"
+with open(input_file_path, "r") as json_file:
+    data = json.load(json_file)
+
+cleaned_data = clean_prefixes(data)
+
+print(cleaned_data['zdj'])
+
+# Save the cleaned data to a new JSON file
+output_file_path = "cn_relations_clean.json"
+with open(output_file_path, "w") as json_file:
+    json.dump(cleaned_data, json_file, ensure_ascii=False, indent=4)

src/cn_inspect.py

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+import json
+from itertools import chain
+
+# Load the relations dictionary from the JSON file
+input_file_path = "cn_relations.json"
+with open(input_file_path, "r") as json_file:
+    relations_dict = json.load(json_file)
+
+# Define the specific language to look for
+specific_language = "ii"
+
+# Check if the specific language exists in the dictionary
+if specific_language in relations_dict:
+    start_nodes = relations_dict[specific_language]
+    print(start_nodes)
+    num_start_edges = sum(len(edges) for edges in start_nodes.values())
+    all_end_edges = list(chain.from_iterable(start_nodes.values()))
+    num_start_edges = len(start_nodes.keys())
+    num_end_edges = len([end_node for edges in start_nodes.values() for end_node in edges])
+    print(f"Language: {specific_language} - Number of start edges: {num_start_edges} - Number of end edges: {num_end_edges}\n")
+
+
+else:
+    print(f"Language {specific_language} not found in the dataset.")

src/cn_parser.py

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+import gzip
+import json
+
+# Define the dictionary to store the relations
+print("--------> Sequential processing setup!")
+relations_dict = {}
+
+# Path to the gzipped file
+file_path = "conceptnet-assertions-5.7.0.csv.gz"
+
+# Function to parse each line and extract words and relations
+def parse_line(line):
+    parts = line.strip().split("\t")
+    if len(parts) >= 3:
+        # Extract the start and end nodes
+        start_node_uri = parts[2]
+        end_node_uri = parts[3]
+        
+        # Extract the language code
+        start_lang_code = start_node_uri.split("/")[2]
+        language_code = start_lang_code
+
+        # Extract the node names
+        start_node = start_node_uri.split("/")[3]
+        end_node = end_node_uri.split("/")[3]
+
+        # Filter out end nodes that are http:// links
+        if end_node_uri.startswith("http://"):
+            return
+
+        # Initialize the language dictionary if it doesn't exist
+        if language_code not in relations_dict:
+            relations_dict[language_code] = {}
+
+        # Initialize the start node list if it doesn't exist
+        if start_node not in relations_dict[language_code]:
+            relations_dict[language_code][start_node] = []
+        # Add the end node to the start node's list
+        relations_dict[language_code][start_node].append(end_node)
+
+# Open the gzipped file and parse each line sequentially
+with gzip.open(file_path, "rt") as f:
+    print("--------> File opened!")
+    for line in f:
+        parse_line(line)
+
+
+# Save the relations dictionary to a JSON file
+print("--------> About to save the file!")
+output_file_path = "cn_relations.json"
+with open(output_file_path, "w") as json_file:
+    json.dump(relations_dict, json_file, ensure_ascii=False)
+
+print("Data saved to", output_file_path)

src/parse_cn.sh

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+#!/bin/bash 
+
+# download ConceptNet data from the official website
+# file name - conceptnet-assertions-5.7.0.csv.gz
+# link - https://s3.amazonaws.com/conceptnet/downloads/2019/edges/conceptnet-assertions-5.7.0.csv.gz
+
+
+echo "Running cn_parser.py..."
+python cn_parser.py
+
+echo "Running cn_inspect.py..."
+python cn_inspect.py
+
+echo "Running cn_clean.py..."
+python cn_clean.py
+
+echo "Running cn_analyze.py..."
+python cn_analyze.py
+
+
+# output files:
+# 1. complete conceptnet data without sources and relation types
+# 2. clean concpenete data without prefixes and underscores between words
+# 3. analysis file with the number of languages and edges for each language