Abstract
Knowledge Graph, as the name says, is a way to represent knowledge using a directed graph structure (nodes and edges). However, such graphs are often incomplete or contain a considerable amount of wrong facts. This work presents ProA: a probabilistic algorithm to predict missing facts from Knowledge Graphs based on the probability distribution over paths between entities. Compared to current state-of-the-art approaches, ProA has the following advantages: simplicity as it considers only the topological structure of a knowledge graph, good performance as it does not require any complex calculations, and readiness as it has no other requirement but the graph itself.
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
Similar content being viewed by others
Notes
- 1.
- 2.
A very well cited paper with over 680 citations in June, 2018.
- 3.
References
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 29–37 (2001)
Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Seman. Web Inf. Syst. 5(3), 1–22 (2009)
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. ACM, Vancouver (2008).https://doi.org/10.1145/1376616.1376746
Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, USA, pp. 2787–2795 (2013)
Brandão, M.A., Moro, M.M.: Social professional networks: a survey and taxonomy. Comput. Commun. 100, 20–31 (2017)
Chekol, M.W., Pirrò, G., Schoenfisch, J., Stuckenschmidt, H.: Marrying uncertainty and time in knowledge graphs. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, pp. 88–94 (2017)
Crichton, G., Guo, Y., Pyysalo, S., Korhonen, A.: Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches. BMC Bioinform. 19(176) (2018)
Georgala, K., Hoffmann, M., Ngomo, A.N.: An evaluation of models for runtime approximation in link discovery. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 57–64 (2017)
Kotnis, B., Nastase, V.: Analysis of the impact of negative sampling on link prediction in knowledge graphs. CoRR (2017)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781
Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016). https://doi.org/10.1109/JPROC.2015.2483592
Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. CoRR abs/1510.04935 (2015). http://arxiv.org/abs/1510.04935
Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on International Conference on Machine Learning, pp. 809–816 (2011)
Nishioka, C., Scherp, A.: Keeping linked open data caches up-to-date by predicting the life-time of RDF triples. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 73–80 (2017)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8, 489–508 (2017)
Sett, N., Chattopadhyay, S., Singh, S.R., Nandi, S.: A time aware method for predicting dull nodes and links in evolving networks for data cleaning. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 304–310 (2016)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of International Conference on World Wide Web, Banff, Canada, pp. 697–706 (2007). https://doi.org/10.1145/1242572.1242667
Trouillon, T., Welbl, J., Riedel, S., Gaussier, E., Bouchard, G.: Complex embeddings for simple link prediction. Int. Conf. Mach. Learn. (ICML) 48, 2071–2080 (2016)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014). https://doi.org/10.1145/2629489
Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. CoRR abs/1412.6575 (2014). http://arxiv.org/abs/1412.6575
Acknowledgements
Work partially funded by CNPq and FAPEMIG, Brazil.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gonzaga, A., Moro, M., Alvim, M.S. (2019). A Probabilistic Algorithm to Predict Missing Facts from Knowledge Graphs. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-27615-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27614-0
Online ISBN: 978-3-030-27615-7
eBook Packages: Computer ScienceComputer Science (R0)