  We present CoDEx, a set of knowledge graph completion datasets extracted from
Wikidata and Wikipedia that improve upon existing knowledge graph completion
benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises
three knowledge graphs varying in size and structure, multilingual descriptions
of entities and relations, and tens of thousands of hard negative triples that
are plausible but verified to be false. To characterize CoDEx, we contribute
thorough empirical analyses and benchmarking experiments. First, we analyze
each CoDEx dataset in terms of logical relation patterns. Next, we report
baseline link prediction and triple classification results on CoDEx for five
extensively tuned embedding models. Finally, we differentiate CoDEx from the
popular FB15K-237 knowledge graph completion dataset by showing that CoDEx
covers more diverse and interpretable content, and is a more difficult link
prediction benchmark. Data, code, and pretrained models are available at
https://bit.ly/2EPbrJs.

Computation and Language;Artificial Intelligence;Information Retrieval;Machine Learning