SchemaTree: Maximum-Likelihood Property Recommendation for Wikidata. Gleim, L. C., Schimassek, R., Hüser, D., Peters, M., Krämer, C., Cochez, M., & Decker, S. In Harth, A., Kirrane, S., Ngonga Ngomo, A., Paulheim, H., Rula, A., Gentile, A. L., Haase, P., & Cochez, M., editors, The Semantic Web, pages 179–195, Cham, 2020. Springer International Publishing.
abstract   bibtex   
Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71%.
@InProceedings{gleim2020schematree,
	author="Gleim, Lars C.
	and Schimassek, Rafael
	and H{\"u}ser, Dominik
	and Peters, Maximilian
	and Kr{\"a}mer, Christoph
	and Cochez, Michael
	and Decker, Stefan",
	editor="Harth, Andreas
	and Kirrane, Sabrina
	and Ngonga Ngomo, Axel-Cyrille
	and Paulheim, Heiko
	and Rula, Anisa
	and Gentile, Anna Lisa
	and Haase, Peter
	and Cochez, Michael",
	title="SchemaTree: Maximum-Likelihood Property Recommendation for {Wikidata}",
	booktitle="The Semantic Web",
	year="2020",
	publisher="Springer International Publishing",
	address="Cham",
	pages="179--195",
	abstract="Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71{\%}.",
	isbn="978-3-030-49461-2"
}
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