To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations. It is challenging to enrich a KG with newly harvested triples while maintaining the quality of the knowledge representation. This paper proposes a system to refine a KG using information harvested from an additional corpus. To this end, we formulate our task as two coupled sub-tasks, namely join event extraction (JEE) and knowledge graph fusion (KGF). We then propose a Collaborative Knowledge Graph Fusion Framework to allow our sub-tasks to mutually assist one another in an alternating manner. More concretely, the explorer carries out the JEE supervised by both the ground-truth annotation and an existing KG provided by the supervisor. The supervisor then evaluates the triples extracted by the explorer and enriches the KG with those that are highly ranked. To implement this evaluation, we further propose a Translated Relation Alignment Scoring Mechanism to align and translate the extracted triples to the prior KG. Experiments verify that this collaboration can both improve the performance of the JEE and the KGF.
翻译:为了从头开始减轻建立知识图(KG)的挑战,一项更一般的任务就是从开放的主体中利用三重知识图(KG)来丰富一个KG,其中获得的三重知识图(KG)包含吵闹的实体和关系;用新收获的三重数据来丰富一个KG,同时保持知识代表质量;本文件提议了一个系统,利用从额外主体中获取的信息来改进KG;为此,我们将我们的任务发展成两个结合的子任务,即合并事件提取(JEE)和知识图聚合(KGF);然后,我们提议一个协作知识图组合框架,使我们的子任务能够交替地相互协助;更具体地说,由探险者进行由地面真相说明和由主管提供的现有KGG监督的JEEE。然后,由主管评估探险者提取的三重数据,用高分级数据来丰富KGG。为了执行这项评估,我们进一步建议一个翻译的Relate Connational Scolation Scorning 机制,以便将提取的三重合为KG。