  A significant part of the largest Knowledge Graph today, the Linked Open Data
cloud, consists of metadata about documents such as publications, news reports,
and other media articles. While the widespread access to the document metadata
is a tremendous advancement, it is yet not so easy to assign semantic
annotations and organize the documents along semantic concepts. Providing
semantic annotations like concepts in SKOS thesauri is a classical research
topic, but typically it is conducted on the full-text of the documents. For the
first time, we offer a systematic comparison of classification approaches to
investigate how far semantic annotations can be conducted using just the
metadata of the documents such as titles published as labels on the Linked Open
Data cloud. We compare the classifications obtained from analyzing the
documents' titles with semantic annotations obtained from analyzing the
full-text. Apart from the prominent text classification baselines kNN and SVM,
we also compare recent techniques of Learning to Rank and neural networks and
revisit the traditional methods logistic regression, Rocchio, and Naive Bayes.
The results show that across three of our four datasets, the performance of the
classifications using only titles reaches over 90% of the quality compared to
the classification performance when using the full-text. Thus, conducting
document classification by just using the titles is a reasonable approach for
automated semantic annotation and opens up new possibilities for enriching
Knowledge Graphs.
