diff --git a/requirements.txt b/requirements.txt
index 20487e1b..f8778227 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,6 +12,5 @@ seaborn
 strsim
 pyecharts
 rltk==2.0.0a20
-kgtk==0.7.1
+kgtk==1.0.1
 Unidecode
-pyrallel.lib==0.0.10
diff --git a/tl/features/cell_context_matches.py b/tl/features/cell_context_matches.py
index 4c3a1e21..a76678ef 100644
--- a/tl/features/cell_context_matches.py
+++ b/tl/features/cell_context_matches.py
@@ -239,6 +239,7 @@ def initialize(self, raw_input_df, context_dict, label_column):
 
         raw_input_df['kg_labels'].fillna("", inplace=True)
         raw_input_df['kg_aliases'].fillna("", inplace=True)
+        raw_input_df['context'].fillna("", inplace=True)
 
         if self.ignore_column is not None:
             _input_df = raw_input_df[(raw_input_df[self.ignore_column].astype(float) == 0)
@@ -386,7 +387,8 @@ def compute_property_scores(self, row_column_pairs: set, n_context_columns: set)
                     int_prop['column'] = col
                     int_prop['col2'] = col2
                     properties_df_list.append(int_prop)
-        properties_df = pd.concat(properties_df_list)
+            if len(properties_df_list) > 0:
+              properties_df = pd.concat(properties_df_list)
         property_value_list = []
         grouped_obj = properties_df.groupby(['column', 'col2', 'property_'])
         for cell, group in grouped_obj: