Abstract
This paper presents and evaluates a method for the detection of DBpedia entity types (classes) that can be used to assess DBpedia’s quality and to complete missing types for un-typed resources. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We evaluate the results for 358 typical DBpedia classes. Our results show that entity embeddings outperform n-gram models for type detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. This is a step toward improving the quality of Linked Open Data in general.
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Acknowledgements
We thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support.
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Zhou, H., Zouaq, A., Inkpen, D. (2017). DBpedia Entity Type Detection Using Entity Embeddings and N-Gram Models. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_21
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