Abstract
Unsupervised fact checking approaches for knowledge graphs commonly combine path search and scoring to predict the likelihood of assertions being true. Current approaches search for said metapaths in the discrete search space spanned by the input knowledge graph and make no use of continuous representations of knowledge graphs. We hypothesize that augmenting existing approaches with information from continuous knowledge graph representations has the potential to improve their performance. Our approach Esther searches for metapaths in compositional embedding spaces instead of the graph itself. By being able to explore longer metapaths, it can detect supplementary evidence for assertions being true that can be exploited by existing fact checking approaches. We evaluate Esther by combining it with 10 other approaches in an ensemble learning setting. Our results agree with our hypothesis and suggest that all other approaches can benefit from being combined with Esther by 20.65% AUC-ROC on average. Our code is open-source and can be found at https://github.com/dice-group/esther.
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Notes
- 1.
We refer to [30] for a survey of KGE techniques.
- 2.
Preliminary tests showed a good performance for the cubic mean in comparison to the arithmetic mean and the quadratic mean.
- 3.
The ontology for FB15k-237 is available at https://github.com/knowledgegraph/schema. The ontology for WN18RR was adapted from https://www.w3.org/2006/03/wn/wn20/. The added information is not taken into account while generating the embeddings.
- 4.
The extended datasets can be found at https://hobbitdata.informatik.uni-leipzig.de/esther/.
- 5.
We use the implementation for TransE and RotatE of https://github.com/DeepGraphLearning/KnowledgeGraphEmbedding and the DensE implementation of https://github.com/anonymous-dense-submission/DensE.
- 6.
For our experiments, we used the source code provided by Shiralkar et al. [22] in the version of October 31st 2018 (see https://github.com/shiralkarprashant/knowledgestream). However, The source code of KL [6] and KL-Rel [22] did not work for us. Hence, a comparison with these approaches was not possible.
- 7.
The runtime experiments were conducted on a system with an Intel®Core™i5-7500 CPU @ 3.40GHz, 16 GB RAM and Ubuntu 20.04.2 LTS.
- 8.
We use WEKA for all meta-algorithms. https://www.cs.waikato.ac.nz/~ml/weka/.
- 9.
We use a Wilcoxon signed rank test with \(\alpha = 0.01\).
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Acknowledgements
This work has been supported by the German Federal Ministry of Education and Research (BMBF) within the EuroStars project E!113314 FROCKG under the grant no 01QE19418. This work has been supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860801.
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da Silva, A.A.M., Röder, M., Ngomo, AC.N. (2021). Using Compositional Embeddings for Fact Checking. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_16
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