@inproceedings{ab1ae35b105f47de9c4bbe9ddaa242a5,
title = "Predicting completeness in knowledge bases",
abstract = "Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where the knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact inference.",
keywords = "Incompleteness, Knowledge bases, Quality, Recall",
author = "Luis Gal{\'a}rraga and Simon Razniewski and Antoine Amarilli and Suchanek, \{Fabian M.\}",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 ; Conference date: 06-02-2017 Through 10-02-2017",
year = "2017",
month = feb,
day = "2",
doi = "10.1145/3018661.3018739",
language = "English",
series = "WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery, Inc",
pages = "375--383",
booktitle = "WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining",
}