Abstract
Knowledge graphs (KGs) use triples to describe real-world facts. They have seen widespread use in intelligent analysis and applications. However, the automatic construction process of KGs unavoidably introduces possible noises and errors. Furthermore, KG-based tasks and applications assume that the knowledge in the KG is entirely correct, which leads to potential deviations. Error detection is critical in KGs, where errors are rare but significant. Various error detection methodologies, primarily path ranking (PR) and representation learning, have been proposed to address this issue. In this paper, we introduced the Enhanced Path Ranking Guided Embedding (EPRGE), which is an improved version of an existing model, the Path Ranking Guided Embedding (PRGE) that uses path-ranking confidence scores to guide TransE embeddings. To improve PRGE, we use a rotational-based embedding model (RotatE) instead of TransE, which uses a self-adversarial negative sampling technique to train the model efficiently and effectively. EPRGE, unlike PRGE, avoids generating meaningless false triples during training by employing the self-adversarial negative sampling method. We compare various methods on two benchmark datasets, demonstrating the potential of our approach and providing enhanced insights on graph embeddings when dealing with noisy KGs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
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 (2008)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Bougiatiotis, K., Fasoulis, R., Aisopos, F., Nentidis, A., Paliouras, G.: Guiding graph embeddings using path-ranking methods for error detection in noisy knowledge graphs. arXiv preprint arXiv:2002.08762 (2020)
Cheng, Y., Chen, L., Yuan, Y., Wang, G.: Rule-based graph repairing: semantic and efficient repairing methods. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 773–784. IEEE (2018)
Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 413–422 (2013)
Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Knowledge graph embedding with iterative guidance from soft rules. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Jia, S., Xiang, Y., Chen, X., Wang, K.: Triple trustworthiness measurement for knowledge graph. In: The World Wide Web Conference, pp. 2865–2871 (2019)
Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)
Lenat, D.B.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)
Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379 (2015)
Melo, A., Paulheim, H.: Detection of relation assertion errors in knowledge graphs. In: Proceedings of the Knowledge Capture Conference, pp. 1–8 (2017)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)
Paulheim, H., Bizer, C.: Improving the quality of linked data using statistical distributions. Int. J. Semant. Web Inf. Syst. (IJSWIS) 10(2), 63–86 (2014)
Tanon, T.P., Stepanova, D., Razniewski, S., Mirza, P., Weikum, G.: Completeness-aware rule learning from knowledge graphs. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 507–525. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_30
Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E., Weikum, G.: YAGO: a multilingual knowledge base from Wikipedia, wordnet, and geonames. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 177–185. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_19
Shah, A., Molokwu, B., Kobti, Z.: HRotatE: hybrid relational rotation embedding for knowledge graph. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2021)
Shan, Y., Bu, C., Liu, X., Ji, S., Li, L.: Confidence-aware negative sampling method for noisy knowledge graph embedding. In: 2018 IEEE International Conference on Big Knowledge (ICBK), pp. 33–40. IEEE (2018)
Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
Wang, M., Qiu, L., Wang, X.: A survey on knowledge graph embeddings for link prediction. Symmetry 13(3), 485 (2021)
Zhang, Q., Dong, J., Duan, K., Huang, X., Liu, Y., Xu, L.: Contrastive knowledge graph error detection. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 2590–2599 (2022)
Acknowledgement
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [funding reference number 03181].
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
Khalil, R., Kobti, Z. (2023). Guided Rotational Graph Embeddings for Error Detection in Noisy Knowledge Graphs. In: Ossowski, S., Sitek, P., Analide, C., Marreiros, G., Chamoso, P., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-031-38333-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-38333-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-38332-8
Online ISBN: 978-3-031-38333-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)