Abstract
Knowledge Graph Reasoning (KGR) is one effective method to improve incompleteness and sparsity problems, which infers new knowledge based on existing knowledge. Although the probabilistic case-based reasoning (CBR) model can predict attributes for an entity and outperform other rule-based and embedding-based methods by gathering reasoning paths from similar entities in KG, it still suffers from some problems such as insufficient graph feature acquisition and omission of contextual relation information. This paper proposes a contextual information-augmented probabilistic CBR model for KGR, namely CICBR. The proposed model frame the reasoning task as the query answering and evaluates the likelihood that a path is valuable at answering a query about the given entity and relation by designing a joint contextual information-obtaining algorithm with entity and relation features. What’s more, to obtain a more fine-grained representation of entity features and relation features, the CICBR introduces Graph Transformer for KG’s representation and learning. Extensive experimental results on various benchmarks prominently demonstrate that the proposed CICBR model can obtain the state-of-the-art results of current CBR-based methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., Mitchell, T.: Toward an architecture for never-ending language learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, no. 1, pp. 1306–1313, July 2010
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250, June 2008
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Behmanesh, S., Talebpour, A., Shamsfard, M., Jafari, M.M.: Improved relation span detection in question answering systems over extracted knowledge bases. Expert Syst. Appl. 224, 119973 (2023)
Lin, R., Tang, F., He, C., Wu, Z., Yuan, C., Tang, Y.: DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning. World Wide Web, pp. 1–23 (2023)
Tailhardat, L., Chabot, Y., Troncy, R.: Designing NORIA: a knowledge graph-based platform for anomaly detection and incident management in ICT systems (2023)
Dubitzky, W., Büchner, A.G., Azuaje, F.J.: Viewing knowledge management as a case-based reasoning application. In: AAAI Workshop Technical Report WS-99-10, pp. 23–27 (1999)
Bartlmae, K., Riemenschneider, M.: Case based reasoning for knowledge management in KDD projects. In: PAKM, October 2000
Das, R., Godbole, A., Dhuliawala, S., Zaheer, M., McCallum, A.: A simple approach to case-based reasoning in knowledge bases (2020). arXiv preprint arXiv:2006.14198
Das, R., Godbole, A., Monath, N., Zaheer, M., McCallum, A.: Probabilistic case-based reasoning for open-world knowledge graph completion (2020). arXiv preprint arXiv:2010.03548
Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs (2020). arXiv preprint arXiv:2012.09699
Pujara, J., Augustine, E., Getoor, L.: Sparsity and noise: where knowledge graph embeddings fall short. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1751–1756, September 2017
Xiong, W., Hoang, T., Wang, W.Y.: Deeppath: a reinforcement learning method for knowledge graph reasoning (2017). arXiv preprint arXiv:1707.06690
Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Jointly embedding knowledge graphs and logical rules. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 192–202, November 2016
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, April 2018
Minervini, P., Demeester, T., Rocktäschel, T., Riedel, S.: Adversarial sets for regularising neural link predictors (2017). arXiv preprint arXiv:1707.07596
García-Durán, A., Niepert, M.: KBLRN: end-to-end learning of knowledge base representations with latent, relational, and numerical features (2017). arXiv preprint arXiv:1709.04676
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Adv. Neural Inf. Process. Syst. 26 (2013)
Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: ICLR (2015)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080. PMLR, June 2016
Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space (2019). arXiv preprint arXiv:1902.10197
Minervini, P., Bošnjak, M., Rocktäschel, T., Riedel, S., Grefenstette, E.: Differentiable reasoning on large knowledge bases and natural language. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 5182–5190, April 2020
Das, R., et al.: Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning (2017). arXiv preprint arXiv:1711.05851
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant No. 62162046, the Inner Mongolia Science and Technology Project under Grant No. 2021GG0155, the Natural Science Foundation of Major Research Plan of Inner Mongolia under Grant No. 2019ZD15, and the Inner Mongolia Natural Science Foundation under Grant No. 2019GG372.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, Y., Zhou, Jt. (2023). A Contextual Information-Augmented Probabilistic Case-Based Reasoning Model for Knowledge Graph Reasoning. In: Massie, S., Chakraborti, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2023. Lecture Notes in Computer Science(), vol 14141. Springer, Cham. https://doi.org/10.1007/978-3-031-40177-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-40177-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40176-3
Online ISBN: 978-3-031-40177-0
eBook Packages: Computer ScienceComputer Science (R0)