  In knowledge bases such as Wikidata, it is possible to assert a large set of
properties for entities, ranging from generic ones such as name and place of
birth to highly profession-specific or background-specific ones such as
doctoral advisor or medical condition. Determining a preference or ranking in
this large set is a challenge in tasks such as prioritisation of edits or
natural-language generation. Most previous approaches to ranking knowledge base
properties are purely data-driven, that is, as we show, mistake frequency for
interestingness.
  In this work, we have developed a human-annotated dataset of 350 preference
judgments among pairs of knowledge base properties for fixed entities. From
this set, we isolate a subset of pairs for which humans show a high level of
agreement (87.5% on average). We show, however, that baseline and
state-of-the-art techniques achieve only 61.3% precision in predicting human
preferences for this subset.
  We then analyze what contributes to one property being rated as more
important than another one, and identify that at least three factors play a
role, namely (i) general frequency, (ii) applicability to similar entities and
(iii) semantic similarity between property and entity. We experimentally
analyze the contribution of each factor and show that a combination of
techniques addressing all the three factors achieves 74% precision on the task.
  The dataset is available at
www.kaggle.com/srazniewski/wikidatapropertyranking.
