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

Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]