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

Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages