  We address the problem of finding descriptive explanations of facts stored in
a knowledge graph. This is important in high-risk domains such as healthcare,
intelligence, etc. where users need additional information for decision making
and is especially crucial for applications that rely on automatically
constructed knowledge bases where machine learned systems extract facts from an
input corpus and working of the extractors is opaque to the end-user. We follow
an approach inspired from information retrieval and propose a simple and
efficient, yet effective solution that takes into account passage level as well
as document level properties to produce a ranked list of passages describing a
given input relation. We test our approach using Wikidata as the knowledge base
and Wikipedia as the source corpus and report results of user studies conducted
to study the effectiveness of our proposed model.
