Abstract
Any data publisher can make RDF knowledge graphs available for consumption on the Web. This is a direct consequence of the decentralized publishing paradigm underlying the Data Web, which has led to more than 150 billion facts on more than 3 billion things being published on the Web in more than 10,000 RDF knowledge graphs over the last decade. However, the success of this publishing paradigm also means that the validation of the facts contained in RDF knowledge graphs has become more important than ever before. Several families of fact validation algorithms have been developed over the last years to address several settings of the fact validation problems. In this paper, we consider the following fact validation setting: Given an RDF knowledge graph, compute the likelihood that a given (novel) fact is true. None of the current solutions to this problem exploits RDFS semantics—especially domain, range and class subsumption information. We address this research gap by presenting an unsupervised approach dubbed COPAAL, that extracts paths from knowledge graphs to corroborate (novel) input facts. Our approach relies on a mutual information measure that takes the RDFS semantics underlying the knowledge graph into consideration. In particular, we use the information shared by predicates and paths within the knowledge graph to compute the likelihood of a fact being corroborated by the knowledge graph. We evaluate our approach extensively using 17 publicly available datasets. Our results indicate that our approach outperforms the state of the art unsupervised approaches significantly by up to 0.15 AUC-ROC. We even outperform supervised approaches by up to 0.07 AUC-ROC. The source code of COPAAL is open-source and is available at https://github.com/dice-group/COPAAL.
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See https://lod-cloud.net/ for data on the growth of the Linked Open Data Cloud.
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We use \(\sqcap \) to denote the conjunction of classes. Note that given that President \(\sqsubseteq \) Person \(\sqsubseteq \) Agent, we could write the type BarackObama in an abbreviated form. Similar considerations holds for the type of UnitedStates. We chose to write the types out to remain consistent with the output of our example knowledge graph, DBpedia 2016-10.
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Preliminary experiments suggest a 20-fold increase in runtime without any significant increase in AUC-ROC.
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If D(p) or R(p) are not available, the types of the given subject or object will be used, respectively.
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We are aware that the terminology (especially concept similarity scores) used in \(\texttt {G}\) can potentially inform the fact validation process further. Studying the integration of assertional and terminological information will be the object of future work and is out of the scope of this paper.
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We used Virtuoso and Fuseki for our experiments and our runtime findings support [7].
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All the datasets and result files can be found at https://hobbitdata.informatik.uni-leipzig.de/COPAAL/.
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award, birthPlace, deathPlace, foundationPlace, leader, team, author, spouse, starring, subsidiary.
References
Athreya, R.G., Ngonga Ngomo, A.C., Usbeck, R.: Enhancing community interactions with data-driven chatbots-the DBpedia chatbot. In: Companion of the the Web Conference 2018 on The Web Conference, pp. 143–146 (2018). International World Wide Web Conferences Steering Committee (2018)
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
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, pp. 2787–2795 (2013)
Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of GSCL, pp. 31–40 (2009)
Ciampaglia, G.L., Shiralkar, P., Rocha, L.M., Bollen, J., Menczer, F., Flammini, A.: Computational fact checking from knowledge networks. PloS One 10(6), e0128193 (2015)
d’Amato, C., Fanizzi, N., Esposito, F.: Inductive learning for the semantic web: what does it buy? Semant. Web 1(1, 2), 53–59 (2010)
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. ACM (2013)
Gerber, D., et al.: DeFacto–temporal and multilingual deep fact validation. Web Semant. Sci. Serv. Agents World Wide Web 35, 85–101 (2015)
Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)
Lehmann, K., Turhan, A.-Y.: A framework for semantic-based similarity measures for \(\cal{ELH}\)-concepts. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds.) JELIA 2012. LNCS (LNAI), vol. 7519, pp. 307–319. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33353-8_24
Lin, P., Song, Q., Wu, Y.: Fact checking in knowledge graphs with ontological subgraph patterns. Data Sci. Eng. 3(4), 341–358 (2018)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Malyshev, S., Krötzsch, M., González, L., Gonsior, J., Bielefeldt, A.: Getting the most out of wikidata: semantic technology usage in wikipedia’s knowledge graph. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11137, pp. 376–394. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00668-6_23
Nickel, M., Tresp, V., Kriegel, H.P.: Factorizing yago: scalable machine learning for linked data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 271–280. ACM (2012)
Paulheim, H., Bizer, C.: Type Inference on noisy RDF data. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 510–525. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_32
Röder, M., Usbeck, R., Ngonga Ngomo, A.: GERBIL - benchmarking named entity recognition and linking consistently. Semant. Web 9(5), 605–625 (2018)
Shi, B., Weninger, T.: Discriminative predicate path mining for fact checking in knowledge graphs. Knowl.-Based Syst. 104, 123–133 (2016)
Shiralkar, P., Flammini, A., Menczer, F., Ciampaglia, G.L.: Finding streams in knowledge graphs to support fact checking. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 859–864. IEEE (2017)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems.,pp. 926–934 (2013)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th InternationalCconference on World Wide Web, pp. 697–706. ACM (2007)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)
Syed, Z.H., Röder, M., Ngonga Ngomo, A.C.: Factcheck: Validating rdf triples using textual evidence. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1599–1602. ACM (2018)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Yin, X., Han, J., Philip, S.Y.: Truth discovery with multiple conflicting information providers on the web. IEEE Trans. Knowl. Data Eng. 20(6), 796–808 (2008)
Zhao, M., Chow, T.W., Zhang, Z., Li, B.: Automatic image annotation via compact graph based semi-supervised learning. Knowl.-Based Syst. 76, 148–165 (2015)
Acknowledgements
This work has been supported by the BMVI projects LIMBO (project no. 19F2029C) and OPAL (project no. 19F20284), the BMBF project SOLIDE (project no. 13N14456) and the EU project KnowGraphs (project no. 860801).
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Syed, Z.H., Röder, M., Ngomo, AC.N. (2019). Unsupervised Discovery of Corroborative Paths for Fact Validation. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_36
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