Knowledge bases of entities and relations (either constructed manually or automatically) are behind many real world search engines, including those at Yahoo!, Microsoft 1 , and Google. Those knowledge bases can be viewed as graphs with nodes representing entities and edges representing (primary) relationships, and various studies have been conducted on how to leverage them to answer entity seeking queries. Meanwhile, in a complementary direction, analyses over the query logs have enabled researchers to identify entity pairs that are statistically correlated. Such entity relationships are then presented to search users through the "related searches" feature in modern search engines. However, entity relationships thus discovered can often be "puzzling" to the users because why the entities are connected is often indescribable. In this paper, we propose a novel problem called entity relationship explanation, which seeks to explain why a pair of entities are connected, and solve this challenging problem by integrating the above two complementary approaches, i.e., we leverage the knowledge base to "explain" the connections discovered between entity pairs. More specifically, we present REX, a system that takes a pair of entities in a given knowledge base as input and efficiently identifies a ranked list of relationship explanations. We formally define relationship explanations and analyze their desirable properties. Furthermore, we design and implement algorithms to efficiently enumerate and rank all relationship explanations based on multiple measures of "interestingness." We perform extensive experiments over real web-scale data gathered from DBpedia and a commercial search engine, demonstrating the efficiency and scalability of REX. We also perform user studies to corroborate the effectiveness of explanations generated by REX.
Fang et al. REF introduces the REX system, which computes a ranked list of entity pairs to describe entity relationships.
REX: Explaining Relationships between Entity Pairs
PVLDB
PVLDB
Computer Science