  Embedded markup of Web pages has seen widespread adoption throughout the past
years driven by standards such as RDFa and Microdata and initiatives such as
schema.org, where recent studies show an adoption by 39% of all Web pages
already in 2016. While this constitutes an important information source for
tasks such as Web search, Web page classification or knowledge graph
augmentation, individual markup nodes are usually sparsely described and often
lack essential information. For instance, from 26 million nodes describing
events within the Common Crawl in 2016, 59% of nodes provide less than six
statements and only 257,000 nodes (0.96%) are typed with more specific event
subtypes. Nevertheless, given the scale and diversity of Web markup data, nodes
that provide missing information can be obtained from the Web in large
quantities, in particular for categorical properties. Such data constitutes
potential training data for inferring missing information to significantly
augment sparsely described nodes. In this work, we introduce a supervised
approach for inferring missing categorical properties in Web markup. Our
experiments, conducted on properties of events and movies, show a performance
of 79% and 83% F1 score correspondingly, significantly outperforming existing
baselines.
