Abstract
Knowledge Graphs are used in an increasing number of applications. Although considerable human effort has been invested into making knowledge graphs available in multiple languages, most knowledge graphs are in English. Additionally, regional facts are often only available in the language of the corresponding region. This lack of multilingual knowledge availability clearly limits the porting of machine learning models to different languages. In this paper, we aim to alleviate this drawback by proposing THOTH, an approach for translating and enriching knowledge graphs. THOTH extracts bilingual alignments between a source and target knowledge graph and learns how to translate from one to the other by relying on two different recurrent neural network models along with knowledge graph embeddings. We evaluated THOTH extrinsically by comparing the German DBpedia with the German translation of the English DBpedia on two tasks: fact checking and entity linking. In addition, we ran a manual intrinsic evaluation of the translation. Our results show that THOTH is a promising approach which achieves a translation accuracy of 88.56%. Moreover, its enrichment improves the quality of the German DBpedia significantly, as we report +18.4% accuracy for fact validation and +19% F\(_1\) for entity linking.
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Notes
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a string or a value with a unit.
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We could not use RDF2Vec in our work as its code was incomplete.
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The black squares represents how the model splits the frequent tokens in a sequence for a better translation process.
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More than one surface forms can be assigned to the entities.
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We selected the subsets of mapping-based objects and labels to evaluate the quality of our approach since they are the most used ones for training Linked-Data NLP approaches.
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We reduced our testset to the first subset of provided abstracts due to evaluation platform limits.
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Acknowledgments
This work has been supported by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) in the projects LIMBO (no. 19F2029I) and OPAL (no. 19F2028A) as well as by the Brazilian National Council for Scientific and Technological Development (CNPq) (no. 206971/2014-1).
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Moussallem, D., Soru, T., Ngonga Ngomo, AC. (2019). THOTH: Neural Translation and Enrichment of Knowledge Graphs. 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_29
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