We study the problem of explaining relationships between pairs of knowledge graph entities with human-readable descriptions. Our method extracts and enriches sentences that refer to an entity pair from a corpus and ranks the sentences according to how well they describe the relationship between the entities. We model this task as a learning to rank problem for sentences and employ a rich set of features. When evaluated on a large set of manually annotated sentences, we find that our method significantly improves over state-of-the-art baseline models.
REF extract human-readable descriptions of relations in a knowledge graph by ranking sentences that justify the relations.
Learning to Explain Entity Relationships in Knowledge Graphs
ACL
Computer Science