Abstract Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BERT or its variants have become necessary in their KGQA models. However, there is still a lack of comprehensive research and comparison of the performance of different PLMs in KGQA. To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. In addition, we present three benchmarks for larger-scale KGs based on the popular SimpleQuestions benchmark to investigate the scalability of PLMs. We carefully analyze the results of all PLMs-based KGQA basic frameworks on these benchmarks and two other popular datasets, WebQuestionSP and FreebaseQA, and find that knowledge distillation techniques and knowledge enhancement methods in PLMs are promising for KGQA. Furthermore, we test ChatGPT, which has drawn a great deal of attention in the NLP community, demonstrating its impressive capabilities and limitations in zero-shot KGQA. We have released the code and benchmarks to promote the use of PLMs on KGQA.
This is the accompanying code & benchmarks for the paper "An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering".
UPDATE(12th March, 2023): the paper has been accepted by World Wide Web Journal.
Please install the following dependency libraries.
- simpletransformers == 0.63.7
- sklearn
- fuzzywuzzy
- nltk
- pytorch == 1.12
- transformers == 4.20.1
- Python version >= 3.7
Important files/folders are described as follows:
PLMs-in-Practical-KBQA/main/
├─ entity_detection/: Entity Detection
├─ ner.py: Training and inferencing PLMS-based NER, except GPT2
├─ train.py: Training and inferencing GPT2-based NER
├─ ner_label.py: Generating labels for similarity-based relation prediction
├─ entity_linking/: Entity Disambiguation based on Vanilla method
├─ entity_linking.py: Disambiguat entity using vanilla method.
├─ entity_disamb/: PLMs-based Entity Disambiguation. Before running the PLMS-based ED model, you need to run entity_linking.py
├─ data_convert.py: Step 1. Converting the form of original dataset
├─ candidate_convert.py: Step 2. Preprocessing the output of entity_linking.py
├─ run_disamb.py: Step 3. Training and inferencing PLMS-based ED.
├─ result_convert.py: Step 4. Evaluating PLMS-based ED results.
├─ rel_prediction/: Classfication-based relation prediction
├─ classify.py: Training
├─ test_re.py: Testing
├─ pathranking/: Pre-processing of similarity-based relation prediction
├─ testdata.py: Constructing dictionary and replacing subject mentions
├─ test_json.py: Generating JSON files for testing
├─ rel_similarity/: Similarity-based relation prediction
├─ train.py: Training and inferencing similarity-based relation prediction
├─ evidence_integration/: Answer Query
├─ final.py: Evaluating the final accuracy
├─ pretrain: PLMs caches.
├─ indexes: All dictionaries
├─ mydata: Dataset
├─ mydata1: Dataset
├─ kb_105M: Benchmark
├─ kb_202M: Benchmark
Please download the related caches before running the code.
Downloading indexes and datas to folders indexes mydata mydata1. https://pan.baidu.com/s/1xpJnGTPnjRXqutK5m_Biiw?pwd=g9op
Downloading indexes for kb_105 kb_202M. https://pan.baidu.com/s/17C1AwWqUmWU4PYUZt-0LzA?pwd=0tpm
Downloading indexes for entity_disamb. https://pan.baidu.com/s/155LUfVYca2UMXAyhBsfUuA?pwd=bdzs
Downloading KGs to folders kb_105M kb_202M. https://drive.google.com/drive/folders/1FMfPYe_BfrlE5psYM7yB8E3iD0ZjGSwH?usp=sharing
Downloading the large scale KG from https://pan.baidu.com/s/1Osm47PFf97eiROPiCB3pXA?pwd=kspe
Downloading PLMs caches to pretrain: BERT, RoBERTa, XLNET, GPT2, ALBERT, DistilBERT, DistilRoBERTa, LUKE, KEPLER
Note that you can following parametes in python file to select PLMs and Benchmarks:
SCALE= BENCHMARK NAME, e.g. small, medium1, medium2, large
TYPE = PLM NAME, e.g. bert, roberta, xlnet, albert, distilbert, distilroberta, luke, kepler
MODEL= PLM CACHE NAME, e.g. bert-base-uncased # names in FOLDER PLMs-in-Practical-KBQA/main/pretrain
If you want to implement KGQA_CL framework, please follow these steps.
(1) Run data_convert.py to generate training data for entity detection
(2) Run ner_label.py to generating labels for similarity-based relation prediction
(3) For GPT2, run train.py in entity_detection folder to train ner; for other models, run ner.py
(1) Run entity_linking.py to generate candidate entities
(2) Run data_convert.py to convert the form of original dataset
(3) Run candidate_convert.py to preprocess the output of entity_linking.py
(4) Run run_disamb.py to train and inference entity disambiguation
(5) Run result_convert.py to evaluate entity disambiguation results
(1) Run classify.py to train
(2) Run test_re.py to test
Run final.py to evaluate the result
If you want to implement KGQA_RR framework, please follow these steps.
Same procedure as classification-based KGQA framework, you can use the results directly
Same procedure as classification-based KGQA framework, you can use the results directly
(1) Run testdata.py to construct dictionary and replace subject mentions
(2) Run test_json.py to generate JSON files for testing
(3) Run train.py in rel_similarity folder to train and inference similarity-based relation prediction
Run final.py to evaluate the result
We have uploaded the our code for using ChatGPT to collect answers to questions in datasets. The code uses the official OpenAI's API . If you want to learn more about APl, see OpenAI's official website for more information: https://platform.openai.com/docs/guides/chat.
Please consider creating a new issue. We will respond to your questions within a few days.