Abstract
Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a low-dimensional space, in which graph structural information are preserved as much as possible, enabling an efficient computation of solutions. We propose a solution for injecting available background knowledge (schema axioms) to further improve the quality of the embeddings. The method has been applied to enhance existing models to produce embeddings that can encode knowledge that is not merely observed but rather derived by reasoning on the available axioms. An experimental evaluation on link prediction and triple classification tasks proves the improvement yielded implementing the proposed method over the original ones.
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Notes
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Facilities available in the Apache Jena framework were used: https://jena.apache.org.
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References
Abboud, R., Ceylan, İ.İ., Lukasiewicz, T., Salvatori, T.: BoxE: a box embedding model for knowledge base completion. In: Proceedings of NeurIPS 2020 (2020)
Arnaout, H., Razniewski, S., Weikum, G.: Enriching knowledge bases with interesting negative statements. In: Das, D., et al. (eds.) Proceedings of AKBC 2020 (2020). https://doi.org/10.24432/C5101K
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C.J.C., et al. (eds.) Proceedings of NIPS 2013, pp. 2787–2795. Curran Associates, Inc. (2013)
Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(09), 1616–1637 (2018). https://doi.org/10.1109/TKDE.2018.2807452
d’Amato, C.: Machine learning for the semantic web: lessons learnt and next research directions. Semant. Web 11(1), 195–203 (2020). https://doi.org/10.3233/SW-200388
Ding, B., Wang, Q., Wang, B., Guo, L.: Improving knowledge graph embedding using simple constraints. In: Proceedings of ACL 2018, vol. 1, pp. 110–121. ACL (2018). https://doi.org/10.18653/v1/P18-1011
Donadello, I., Serafini, L.: Compensating supervision incompleteness with prior knowledge in semantic image interpretation. In: Proceedings of IJCNN 2019, pp. 1–8. IEEE (2019). https://doi.org/10.1109/IJCNN.2019.8852413
Dong, X.L., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of KDD 2014, pp. 601–610 (2014)
Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Jointly embedding knowledge graphs and logical rules. In: Proceedings of EMNLP 2016, pp. 192–202. ACL (2016). https://doi.org/10.18653/v1/D16-1019
Gutiérrez-Basulto, V., Schockaert, S.: From knowledge graph embedding to ontology embedding? An analysis of the compatibility between vector space representations and rules. In: Thielscher, M., Toni, F., Wolter, F. (eds.) Proceedings of KR 2018, pp. 379–388. AAAI Press (2018)
He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of CIKM 2015, pp. 623–632. ACM (2015). https://doi.org/10.1145/2806416.2806502
Hogan, A., et al.: Knowledge graphs. arXiv:2003.02320 (2020)
Jayathilaka, M., Mu, T., Sattler, U.: Visual-semantic embedding model informed by structured knowledge. In: Rudolph, S., Marreiros, G. (eds.) Proceedings of STAIRS 2020. CEUR, vol. 2655. CEUR-WS.org (2020). http://ceur-ws.org/Vol-2655/paper23.pdf
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of ACL-IJCNLP 2015, vol. 1, pp. 687–696. ACL (2015). https://doi.org/10.3115/v1/P15-1067
Ji, S., Pan, S., Cambria, E., Marttinen, P., Yu, P.S.: A survey on knowledge graphs: representation, acquisition and applications. arXiv:2002.00388 (2020)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI 2015 Proceedings, pp. 2181–2187. AAAI Press (2015)
Liu, Y., Li, H., Garcia-Duran, A., Niepert, M., Onoro-Rubio, D., Rosenblum, D.S.: MMKG: multi-modal knowledge graphs. In: Hitzler, P., et al. (eds.) ESWC 2019. LNCS, vol. 11503, pp. 459–474. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21348-0_30
Lv, X., Hou, L., Li, J., Liu, Z.: Differentiating concepts and instances for knowledge graph embedding. In: Riloff, E., et al. (eds.) Proceedings of EMNLP 2018, pp. 1971–1979. ACL (2018). https://doi.org/10.18653/v1/D18-1222
Minervini, P., Costabello, L., Muñoz, E., Nováček, V., Vandenbussche, P.-Y.: Regularizing knowledge graph embeddings via equivalence and inversion axioms. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 668–683. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71249-9_40
Minervini, P., d’Amato, C., Fanizzi, N.: Efficient energy-based embedding models for link prediction in knowledge graphs. J. Intell. Inf. Syst. 47(1), 91–109 (2016). https://doi.org/10.1007/s10844-016-0414-7
Minervini, P., Demeester, T., Rocktäschel, T., Riedel, S.: Adversarial sets for regularising neural link predictors. In: Elidan, G., et al. (eds.) UAI 2017 Proceedings. AUAI Press (2017)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8, 489–508 (2016). https://doi.org/10.3233/SW-160218
Paulheim, H.: Make embeddings semantic again! In: Proceedings of the ISWC 2018 P&D-Industry-BlueSky Tracks. CEUR Workshop Proceedings (2018)
Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of NIPS 2013, pp. 926–934 (2013)
Sun, Z., Huang, J., Hu, W., Chen, M., Guo, L., Qu, Y.: TransEdge: translating relation-contextualized embeddings for knowledge graphs. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 612–629. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_35
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI 2014, pp. 1112–1119. AAAI Press (2014)
Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of ICLR 2015 (2015)
Acknowledgment
We would like to thank Giovanni Sansaro who formalized and developed the code for the preliminary version of TransOWL for his bachelor thesis.
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d’Amato, C., Quatraro, N.F., Fanizzi, N. (2021). Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs. In: Verborgh, R., et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_26
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