Abstract
Clustering entities over knowledge graphs (KGs) is an asset for explorative search and knowledge discovery. KG embeddings have been intensively investigated, mostly for KG completion, and have potential also for entity clustering. However, embeddings are latent and do not convey user-interpretable labels for clusters. This work presents ExCut, a novel approach that combines KG embeddings with rule mining methods, to compute informative clusters of entities along with comprehensible explanations. The explanations are in the form of concise combinations of entity relations. ExCut jointly enhances the quality of entity clusters and their explanations, in an iterative manner that interleaves the learning of embeddings and rules. Experiments on real-world KGs demonstrate the effectiveness of ExCut for discovering high-quality clusters and their explanations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Code, data and the technical report are available at https://github.com/mhmgad/ExCut.
References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NeurIPS, pp. 2787–2795 (2013)
Bouraoui, Z., Schockaert, S.: Learning conceptual space representations of interrelated concepts. In: IJCAI, pp. 1760–1766 (2018)
Costabello, L., Pai, S., Van, C.L., McGrath, R., McCarthy, N., Tabacof, P.: AmpliGraph: a library for representation learning on knowledge graphs (2019)
Dumancic, S., Blockeel, H.: An expressive dissimilarity measure for relational clustering over neighbourhood trees. MLJ (2017)
Dumancic, S., García-Durán, A., Niepert, M.: On embeddings as an alternative paradigm for relational learning. CoRR arXiv:abs/1806.11391v2 (2018)
Fanizzi, N., d’Amato, C., Esposito, F.: Conceptual clustering and its application to concept Drift and Novelty Detection. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 318–332. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68234-9_25
Fonseca, N.A., Costa, V.S., Camacho, R.: Conceptual clustering of multi-relational data. In: ILP, pp. 145–159 (2011)
Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707–730 (2015)
Guo, Y., Pan, Z., Heflin, J.: LUBM: a benchmark for OWL knowledge base systems. J. Web Semant. 3(2–3), 158–182 (2005)
Hamilton, W.L., Bajaj, P., Zitnik, M., Jurafsky, D., Leskovec, J.: Embedding logical queries on knowledge graphs. In: NeurIPS, pp. 2030–2041 (2018)
Ho, V.T., Stepanova, D., Gad-Elrab, M.H., Kharlamov, E., Weikum, G.: Rule learning from knowledge graphs guided by embedding models. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 72–90. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_5
Idahl, M., Khosla, M., Anand, A.: Finding interpretable concept spaces in node embeddings using knowledge bases. In: Cellier, P., Driessens, K. (eds.) ML/KDD, pp. 229–240 (2020)
Knobbe, A.J., Ho, E.K.Y.: Pattern teams. In: PKDD, pp. 577–584 (2006)
Lavrač, N., Flach, P., Zupan, B.: Rule evaluation measures: a unifying view. In: ILP, pp. 174–185 (1999)
Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: EMNLP, pp. 705–714 (2015)
Lisi, F.A.: A pattern-based approach to conceptual clustering in FOL. In: Schärfe, H., Hitzler, P., Øhrstrøm, P. (eds.) ICCS-ConceptStruct 2006. LNCS (LNAI), vol. 4068, pp. 346–359. Springer, Heidelberg (2006). https://doi.org/10.1007/11787181_25
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Meilicke, C., Chekol, M.W., Ruffinelli, D., Stuckenschmidt, H.: Anytime bottom-up rule learning for knowledge graph completion. In: IJCAI, pp. 3137–3143 (2019)
Meilicke, C., Fink, M., Wang, Y., Ruffinelli, D., Gemulla, R., Stuckenschmidt, H.: Fine-grained evaluation of rule- and embedding-based systems for knowledge graph completion. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 3–20. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_1
Omran, P.G., Wang, K., Wang, Z.: An embedding-based approach to rule learning in knowledge graphs. IEEE Trans. Knowl. Data Eng. 1–1 (2019)
Omran, P.G., Wang, K., Wang, Z.: Scalable rule learning via learning representation. In: IJCAI, pp. 2149–2155 (2018)
Ortona, S., Meduri, V.V., Papotti, P.: Robust discovery of positive and negative rules in knowledge bases. In: ICDE, pp. 1168–1179. IEEE (2018)
Ren, H., Hu, W., Leskovec, J.: Query2box: Reasoning over knowledge graphs in vector space using box embeddings. In: ICLR (2020)
Ristoski, P., Paulheim, H.: A comparison of propositionalization strategies for creating features from linked open data. In: 1st Linked Data for Knowledge Discovery Workshop at ECML/PKDDAt: Nancy, France (2014)
Pérez-Suárez, A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: A review of conceptual clustering algorithms. Artif. Intell. Rev. 52(2), 1267–1296 (2018). https://doi.org/10.1007/s10462-018-9627-1
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: Proceedings of WWW, pp. 697–706 (2007)
Tiddi, I., d’Aquin, M., Motta, E.: Dedalo: Looking for clusters explanations in a labyrinth of linked data. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) The Semantic Web: Trends and Challenges (ESWC 2014). Lecture Notes in Computer Science, vol. 8465, pp. 333–348. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_23
Tiddi, I., d’Aquin, M., Motta, E.: Data patterns explained with linked data. In: Bifet, A., et al. (eds.) Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2015). Lecture Notes in Computer Science, vol. 9286, pp. 271–275. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23461-8_28
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071–2080 (2016)
Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., Zhang, C.: Attributed graph clustering: a deep attentional embedding approach. In: IJCAI, pp. 3670–3676 (2019)
Xie, J., Girshick, R.B., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: ICML, pp. 478–487 (2016)
Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of KGs with entity descriptions. In: AAAI, pp. 2659–2665 (2016)
Yang, F., Yang, Z., Cohen, W.W.: Differentiable learning of logical rules for knowledge base reasoning. In: NeurIPS, pp. 2319–2328 (2017)
Zhang, W., et al.: Iteratively learning embeddings and rules for knowledge graph reasoning. In: WWW, pp. 2366–2377 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gad-Elrab, M.H., Stepanova, D., Tran, TK., Adel, H., Weikum, G. (2020). ExCut: Explainable Embedding-Based Clustering over Knowledge Graphs. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-62419-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62418-7
Online ISBN: 978-3-030-62419-4
eBook Packages: Computer ScienceComputer Science (R0)
