Abstract
Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at https://github.com/tamlhp/kbqa.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chakraborty, N., Lukovnikov, D., Maheshwari, G., Trivedi, P., et al.: Introduction to neural network-based question answering over knowledge graphs. WIREs DMKD 11(3), e1389 (2021)
Costa, J.O., Kulkarni, A.: Leveraging knowledge graph for open-domain question answering. In: WI, pp. 389–394 (2018)
Dubey, M., Banerjee, D., Chaudhuri, D., Lehmann, J.: EARL: joint entity and relation linking for question answering over knowledge graphs. In: ISWC, pp. 108–126 (2018)
Gao, Y., Tian, X., Zhou, J., Zheng, B., Li, H., Zhu, Z.: Knowledge graph embedding based on quaternion transformation and convolutional neural network. In: ADMA, pp. 128–136 (2022)
Hu, S., Zou, L., Yu, J.X., Wang, H., Zhao, D.: Answering natural language questions by subgraph matching over knowledge graphs. TKDE 30(5), 824–837 (2018)
Hung, N.Q.V., Tam, N.T., Tran, L.N., Aberer, K.: An evaluation of aggregation techniques in crowdsourcing. In: WISE, pp. 1–15 (2013)
Liang, S., Stockinger, K., de Farias, T.M., Anisimova, M., Gil, M.: Querying knowledge graphs in natural language. J. Big Data 8(1), 1–23 (2021). https://doi.org/10.1186/s40537-020-00383-w
Ma, J., Zhong, M., Wen, J., Chen, W., Zhou, X., Li, X.: RecKGC: integrating recommendation with knowledge graph completion. In: ADMA, pp. 250–265 (2019)
Nguyen, T.T., et al.: Monitoring agriculture areas with satellite images and deep learning. Appl. Soft Comput. 95, 106565 (2020)
Park, J., Cho, Y., Lee, H., Choo, J., Choi, E.: A knowledge graph-based question answering with electronic health records. In: MLHC, vol. 149, pp. 1–17 (2021)
Pomerantz, J.: A linguistic analysis of question taxonomies: research articles. J. Assoc. Inf. Sci. Technol. 56(7), 715–728 (2005)
Toan, N.T., Cong, P.T., Hung, N.Q.V., Jo, J.: A deep learning approach for early wildfire detection from hyperspectral satellite images. In: RiTA, pp. 38–45 (2019)
Trivedi, P., Maheshwari, G., Dubey, M., Lehmann, J.: LC-QuAD: a corpus for complex question answering over knowledge graphs. In: ISWC, pp. 210–218 (2017)
Usbeck, R., Gusmita, R.H., Ngomo, A-C.N., Saleem, M.: 9th challenge on question answering over linked data (QALD-9). In: ISWC, pp. 58–64 (2018)
Vollmers, D., Jalota, R., Moussallem, D., Topiwala, H., Ngomo, A.C.N., Usbeck, R.: Knowledge graph question answering using graph-pattern isomorphism. arXiv preprint arXiv:2103.06752 (2021)
Wang, P., et al.: Text-to-SQL generation for question answering on electronic medical records. In: WWW, pp. 350–361 (2020)
Weikum, G.: Knowledge graphs 2021: a data odyssey. PVLDB 14(12), 3233–3238 (2021)
Zheng, Y., et al.: Quality prediction of newly proposed questions in CQA by leveraging weakly supervised learning. In: ADMA, pp. 655–667 (2017)
Acknowledgement
This research is funded by University of Information Technology, Vietnam National University HoChiMinh City under grant number D1-2022–25.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tran, K.V., Phan, H.P., Quach, K.N.D., Nguyen, N.LT., Jo, J., Nguyen, T.T. (2022). A Comparative Study of Question Answering over Knowledge Bases. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-22064-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22063-0
Online ISBN: 978-3-031-22064-7
eBook Packages: Computer ScienceComputer Science (R0)