REX：解释实体对之间的关系
REX: Explaining Relationships between Entity Pairs
摘要：
实体和关系的知识库（手动或自动构建）在许多现实世界的搜索引擎（包括Yahoo！微软和谷歌。这些知识库可以被视为具有表示实体的节点和表示（主要）关系的边的图，并且已经进行了关于如何利用它们来回答实体搜索查询的各种研究。与此同时，在一个互补的方向上，对查询日志的分析使研究人员能够识别统计相关的实体对。这样的实体关系然后通过现代搜索引擎中的“相关搜索”特征呈现给搜索用户。然而，这样发现的实体关系对于用户来说常常是“令人困惑的”，因为为什么实体被连接常常是难以描述的。在本文中，我们提出了一个称为“实体关系解释”的新问题，该问题旨在解释为什么一对实体是连接的，并通过整合上述两种互补方法来解决这个具有挑战性的问题，即，我们利用知识库来“解释”在实体对之间发现的连接。更具体地说，我们提出了REX，一个系统，需要一对实体在一个给定的知识库作为输入，并有效地确定一个排名列表的关系解释。我们正式定义关系的解释和分析其可取的属性。此外，我们设计和实现算法，有效地枚举和排名的基础上的多个措施的“兴趣”的所有关系的解释。“我们对从DBpedia和商业搜索引擎收集的真实网络规模数据进行了广泛的实验，证明了REX的效率和可扩展性。我们还进行用户研究，以证实REX生成的解释的有效性。
英文摘要：
Knowledge bases of entities and relations (either constructed manually or automatically) are behind many real world search engines, including those at Yahoo!, Microsoft, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
