Code repo for Conditional Focused Neural Question Answering with Large-scale Knowledge Bases

1. Refer to Virtuoso.md to install and confiture the software
2. Make sure torch7 is installed together with the following dependencies
   • logroll: luarocks install logroll
   • nngraph: luarocks install nngraph
3. After the installation and configuration of Virtuoso, run bash data_preprocess.sh to finish preprocessing

1. Focused Lableing

   cd FocusedLabeling
   th train_crf.lua
   

2. Entity Type Vector

   cd EntityTypeVec
   th train_ent_typevec.lua
   

3. RNN based Relation Network

   cd RelationRNN
   th train_rel_rnn.lua
   

In the following, define SPLIT='valid' or 'test'.

1. Run focused labeling on validation/test data

   cd FocusedLabeling
   
   python generate_inference_data.py --split ${SPLIT}
   
   th process_inference.lua -testSplit ${SPLIT}
   th infer_crf.lua \
       -testData inference-data/label.${SPLIT}.t7 \
       -modelFile "path-to-pretrained-model"
   

   • python generate_inference_data.py --split ${SPLIT} will create the file label.${SPLIT}.txt in the folder FocusedLabeling/inference-data;
   • th process_inference.lua will turn the text file label.${SPLIT}.txt into label.${SPLIT}.t7 in torch format (both in the folder FocusedLabeling/inference-data);
   • th infer_crf.lua ... will generate the file label.result.${SPLIT} in the folder FocusedLabeling.

2. Query candidates based on focused labeling

   cd Inference
   mkdir ${SPLIT} && cd ${SPLIT}
   python ../query_candidates.py 6 \
          ../../PreprocessData/QAData.${SPLIT}.pkl \
          ../../FocusedLabeling/label.result.${SPLIT} \
          ../../KnowledgeBase/type.top-500.pkl
   

   This step will generate the file QAData.label.${SPLIT}.cpickle in the folder Inference/${SPLIT}.

3. Generate score data based on the query results

   cd Inference/${SPLIT}
   python ../generate_score_data.py QAData.label.${SPLIT}.cpickle
   

   This step will generate the following files in the same folder Inference/${SPLIT}:

   • rel.single.${SPLIT}.txt (candidate relations for those with only a single candidate subject)
   • rel.multi.${SPLIT}.txt (candidate relations for those with only multiple candidate subject)
   • type.multi.${SPLIT}.txt (candidate entities for those with multiple candidate subjects)
   • single.${SPLIT}.cpickle
   • multi.${SPLIT}.cpickle

4. Run relation inference

   cd RelationRNN
   mkdir inference-data
   th process_inference.lua -testSplit ${SPLIT}
   th infer_rel_rnn.lua -testData inference-data/rel.single.${SPLIT}.t7
   th infer_rel_rnn.lua -testData inference-data/rel.multi.${SPLIT}.t7
   

   This step will generate the files score.rel.single.${SPLIT} and score.rel.multi.${SPLIT} in the folder RelationRNN.

5. Run entity inference

   cd EntityTypeVec
   mkdir inference-data
   th process_inference.lua -testSplit ${SPLIT}
   th infer_ent_typevec.lua -testData inference-data/ent.${SPLIT}.t7
   

   This step will generate the file score.ent.multi.multi.${SPLIT} in the folder EntityTypeVec.

6. Run joint disambiguation

   cd Inference/${SPLIT}
   python ../joint_disambiguation.py multi.${SPLIT}.cpickle \
          ../../RelationRNN/score.rel.multi.${SPLIT} \
          ../../EntityTypeVec/score.ent.multi.multi.${SPLIT}