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An empirical study of pre-trained language models in simple knowledge graph question answering

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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 (https://chat.openai.com/), 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 (https://github.com/aannonymouuss/PLMs-in-Practical-KBQA).

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Data Availability

All datasets and codes in this paper can be accessed from https://github.com/aannonymouuss/PLMs-in-Practical-KBQA.

Notes

  1. Freebase [38] contains over 3 billion triples across over 100 domains, while Google Knowledge Graph [39] has amassed over 500 billion triples.

  2. These tasks are similar to named entity recognition, entity linking and relation extraction.

  3. Scalability is the measure of a system’s ability to increase or decrease in performance and cost in response to changes in system processing demands. In our work, we explore the variation in accuracy performance and time cost with increasing KG size.

  4. Available online: https://keywordtool.io/blog/most-asked-questions/ (accessed on 12 April 2022)

  5. There is an existing KGQA approach based on KG embedding, which introduces knowledge representation learning, is proposed by [23] and is not included in our frameworks. This work focuses on comparing various PLMs, so the discussion of the effect of different KG embedding methods is reserved for future work.

  6. Our basic frameworks are trained using an NVIDIA GeForce RTX 2080 TI

  7. The dimension of h is \(1\times 1\). Different PLMs obtain h in different ways, e.g. \(h = w\cdot h_{\left[ CLS\right] }^{T}\) in BERT.

  8. http://lemurproject.org/clueweb12/FACC1/

  9. https://developers.google.com/freebase

  10. https://huggingface.co/bert-base-uncased.

  11. https://huggingface.co/roberta-base.

  12. https://huggingface.co/xlnet-base-cased.

  13. https://huggingface.co/gpt2.

  14. https://huggingface.co/albert-base-v2.

  15. https://huggingface.co/distilbert-base-uncased.

  16. https://huggingface.co/distilroberta-base.

  17. https://huggingface.co/studio-ousia/luke-base.

  18. https://github.com/THU-KEG/KEPLER.

  19. These data are from https://huggingface.co/.

  20. The pre-processed datasets are available at https://github.com/aistairc/simple-qa-analysis.

  21. The version of ChatGPT is Jau 30 Version, and the user’s access times are limited. We have released the script for accessing ChatGPT.

  22. We have also tried to generate concise answers like entity names by specific instructions, but it leads to worse performance.

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Funding

This work is supported by National Nature Science Foundation of China (No. U21A20488).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, 2 Dongda Rd, 211189, Nanjing, Jiangsu, China

    Nan Hu, Yike Wu, Guilin Qi, Dehai Min & Zafar Ali

  2. Department of Computer Science, The University of Manchester, Oxford Rd, M13 9PL, Manchester, UK

    Jiaoyan Chen

  3. School of Informatics, The University of Edinburgh, 10 Crichton St, 2EH8 9AB, Edinburgh, UK

    Jeff Z Pan

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Contributions

Nan Hu: Conceptualization, Methodology, Software, Writing - Original Draft. Yike Wu: Investigation, Software, Validation. Guilin Qi: Conceptualization, Methodology, Writing - review & editing. Dehai Min: Software. Jiaoyan Chen: Writing - review & editing. Jeff Z. Pan: Writing - review & editing. Zafar Ali: Validation.

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Correspondence to Guilin Qi.

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Hu, N., Wu, Y., Qi, G. et al. An empirical study of pre-trained language models in simple knowledge graph question answering. World Wide Web 26, 2855–2886 (2023). https://doi.org/10.1007/s11280-023-01166-y

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