KG4Py: A toolkit for generating Python knowledge graph and code semantic search

References

• Abdelaziz, I., Dolby, J., McCusker, J., & Srinivas, K. (2021). A toolkit for generating code knowledge graphs. In Proceedings of the 11th on knowledge capture conference (pp. 137–144). https://doi.org/10.1145/3460210.3493578
   Google Scholar
• Abdelaziz, I., Srinivas, K., Dolby, J., & McCusker, J. P. (2020). A demonstration of CodeBreaker: A machine interpretable knowledge graph for code. In ISWC (Demos/Industry). http://ceur-ws.org/Vol-2721/paper568.pdf
   Google Scholar
• Allamanis, M. (2019). The adverse effects of code duplication in machine learning models of code. In Proceedings of the 2019 ACM SIGPLAN international symposium on new ideas, new paradigms, and reflections on programming and software (pp. 143–153). https://doi.org/10.1145/3359591.3359735
   Google Scholar
• Arul, J. J. A., & Razia, S. A. (2021). A review on the literature of fashion recommender system using deep learning. International Journal of Performability Engineering, 17(8), 695–702. https://doi.org/10.23940/ijpe.21.08.p5.695702
   Google Scholar
• Chen, H., Hu, N., Qi, G., Wang, H., Bi, Z., Li, J., & Yang, F. (2021). Openkg chain: A blockchain infrastructure for open knowledge graphs. Data Intelligence, 3(2), 205–227. https://doi.org/10.1162/dint_a_00095
   Google Scholar
• Denny, V. C., & Markus, K. (2014). Wikidata: A free collaborative knowledgebase. Communications of the Acm, 57(10), 78–85. https://doi.org/10.1145/2629489
   Web of Science ®Google Scholar
• Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv, 1810, 04805.
   Google Scholar
• Do, T. T. T., & Nguyen, D. T. (2021). A computational semantic information retrieval model for Vietnamese texts. International Journal of Computational Science and Engineering, 24(3), 301–311. https://www.inderscienceonline.com/doi/abs/10.1504/IJCSE.2021.115657
   Web of Science ®Google Scholar
• Fang, W., Jiang, T., Jiang, K., Zhang, F., Ding, Y., & Sheng, J. (2020). A method of automatic text summarisation based on long short-term memory. International Journal of Computational Science and Engineering, 22(1), 39–49. https://doi.org/10.1504/IJCSE.2020.107243
   Web of Science ®Google Scholar
• Feng, S., Chen, H., Huang, M., & Wu, Y. (2021). Intelligent question answering system based on entrepreneurial incubation knowledge graph. In 2021 4th international conference on pattern recognition and artificial intelligence (PRAI) (pp. 207–212). IEEE. https://doi.org/10.1109/PRAI53619.2021.9551028
   Google Scholar
• Feng, Z., Guo, D., Tang, D., & Gong, M. (2020). Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155.
   Google Scholar
• Guo, D., Ren, S., Feng, Z., Tang, D., & Liu, S. (2020). Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366.
   Google Scholar
• Hao, J., Zhao, L., Milisavljevic-Syed, J., & Ming, Z. (2021). Integrating and navigating engineering design decision-related knowledge using decision knowledge graph. Advanced Engineering Informatics, 50. https://doi.org/10.1016/j.aei.2021.101366
   Web of Science ®Google Scholar
• Husain, H., Wu, H. H., Gazit, T., Allamanis, M., & Brockschmidt, M. (2019). Codesearchnet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436.
   Google Scholar
• Jiang, X., Zheng, Z., Lyu, C., Li, L., & Lyu, L. (2021). TreeBERT: A Tree-Based Pre-Trained Model for Programming Language. arXiv preprint arXiv:2105.12485.
   Google Scholar
• Kurt, B., Colin, E., Praveen, P., Tim, S., & Jamie, T. (2008). Freebase: A collaboratively created graph database for structuring human knowledge. In SIGMOD conference (pp. 1247–1250). https://doi.org/10.1145/1376616.1376746
   Google Scholar
• Li, G., & Zhao, T. (2021). Approach of intelligence question-answering system based on physical fitness knowledge graph. In 2021 4th international conference on robotics, control and automation engineering (RCAE) (pp. 191–195). IEEE. https://doi.org/10.1109/rcae53607.2021.9638824
   Google Scholar
• Lian, S., & Tang, M. (2022). API recommendation for Mashup creation based on neural graph collaborative filtering. Connection Science, 34(1), 124–138. https://doi.org/10.1080/09540091.2021.1974819
   Web of Science ®Google Scholar
• Lin, N., Li, J., & Jiang, S. (2022). A simple but effective method for Indonesian automatic text summarisation. Connection Science, 34(1), 29–43. https://doi.org/10.1080/09540091.2021.1937942
   Web of Science ®Google Scholar
• Liu, M., Peng, X., Marcus, A., Xing, Z., Xie, W., Xing, S., & Liu, Y. (2019). Generating query-specific class API summaries. In Proceedings of the 2019 27th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering (pp. 120–130). https://doi.org/10.1145/3338906.3338971
   Google Scholar
• Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., … Liu, S. (2021). CodeXGLUE: A machine learning benchmark dataset for code understanding and generation. arXiv preprint arXiv:2102.04664.
   Google Scholar
• Manu, S., Julian, D., Satish, C., Max, S., & Frank, T. (2012). Correlation tracking for points-to analysis of JavaScript. In ECOOP 2012 – object-oriented programming – 26th European conference, Beijing, China, June 11–16, 2012. Proceedings (lecture notes in computer science, vol. 7313). Springer, pp. 435–458. https://doi.org/10.1007/978-3-642-31057-7_20
   Google Scholar
• Meng, G., Xue, Y., Siow, J. K., Su, T., Narayanan, A., & Liu, Y. (2017). Androvault: Constructing knowledge graph from millions of android apps for automated analysis. arXiv preprint arXiv:1711.07451.
   Google Scholar
• Mir, A. M., Latoskinas, E., & Gousios, G. (2021a). ManyTypes4Py: A benchmark python dataset for machine learning-based type inference. arXiv preprint arXiv:2104.04706.
   Google Scholar
• Mir, A. M., Latoskinas, E., Proksch, S., & Gousios, G. (2021b). Type4py: Deep similarity learning-based type inference for Python. arXiv preprint arXiv:2101.04470.
   Google Scholar
• Qiao, X., Luo, L., Yang, J., & Hu, Z. (2020). Intelligent recommendation method of sous-vide cooking dishes correlation analysis based on association rules mining. International Journal of Performability Engineering, 16(9), 1443–1450. https://doi.org/10.23940/ijpe.20.09.p13.14431450
   Google Scholar
• Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.
   Google Scholar
• Tang, B., Tang, M., Xia, Y., & Hsieh, M. Y. (2021). Composition pattern-aware web service recommendation based on depth factorisation machine. Connection Science, 33(4), 870–890. https://doi.org/10.1080/09540091.2021.1911933
   Web of Science ®Google Scholar
• Thakur, N., Reimers, N., Rücklé, A., Srivastava, A., & Gurevych, I. (2021). BEIR: A heterogenous benchmark for zero-shot evaluation of information retrieval models. arXiv preprint arXiv:2104.08663.
   Google Scholar
• Wang, F., Liu, J. P., Liu, B., Qian, T. Y., Xiao, Y. H., & Peng, Z. Y. (2020a). Survey on construction of code knowledge graph and intelligent software development. Journal of Software, 31(1), 47–66. http://www.jos.org.cn/1000-9825/5893.htm
   Google Scholar
• Wang, J., Shi, X., Cheng, L., Zhang, K., & Shi, Y. (2020b). SoftKG: Building a software development knowledge graph through wikipedia taxonomy. In 2020 IEEE world congress on services (SERVICES) (pp. 151–156). IEEE. https://doi.org/10.1109/SERVICES48979.2020.00042
   Google Scholar
• Wang, M., Zou, Y., Cao, Y., & Xie, B. (2019). Searching software knowledge graph with question. In International conference on software and systems reuse (pp. 115–131). Springer. https://doi.org/10.1007/978-3-030-22888-0_9
   Google Scholar
• Wang, Y., Wang, W., Joty, S., & Hoi, S. C. (2021). CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In Proceedings of the 2021 conference on empirical methods in natural language processing (pp. 8696–8708). https://doi.org/10.18653/v1/2021.emnlp-main.685
   Google Scholar
• Xie, L., Hu, Z., Cai, X., Zhang, W., & Chen, J. (2021). Explainable recommendation based on knowledge graph and multi-objective optimization. Complex & Intelligent Systems, 7(3), 1241–1252. https://doi.org/10.1007/s40747-021-00315-y
   Web of Science ®Google Scholar
• Yu, D., Chen, R., & Chen, J. (2020). Video recommendation algorithm based on knowledge graph and collaborative filtering. International Journal of Performability Engineering, 16(12), 1933–1940. https://doi.org/10.23940/ijpe.20.12.p9.19331940
   Google Scholar
• Zhang, X., Lu, W., Li, F., Zhang, R., & Cheng, J. (2020). A deep neural architecture for sentence semantic matching. International Journal of Computational Science and Engineering, 21(4), 574–582. https://doi.org/10.1504/IJCSE.2020.106870
   Web of Science ®Google Scholar
• Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018). Variational reasoning for question answering with knowledge graph. In Thirty-second AAAI conference on artificial intelligence. https://ojs.aaai.org/index.php/AAAI/article/view/12057
   Google Scholar
• Zhao, X., Chen, H., Xing, Z., & Miao, C. (2021). Brain-inspired search engine assistant based on knowledge graph. IEEE Transactions on Neural Networks and Learning Systems, 1–15. https://doi.org/10.1109/tnnls.2021.3113026
   Google Scholar