CaSIE: Canonicalize and Informative Selection of the OpenIE system

Knowledge extraction has become a hot topic recently with the increasing number of applications needed for large-scale knowledge bases (KBs), such as semantic search and QA systems. The goal of knowledge extraction is to extract relations and their arguments from natural language text. Recent research proposes two kinds of solutions. The first one, called Closed IE, tries to construct KB through predefined features or rules with respect to a specific domain. It requires specifying the interested predicates in advance, which restricts its application to the domains where prior knowledge about the interested predicates must be given. The second one, called Open IE, tries to extract facts by using the parsing structure from the unstructured text. However, they cannot avoid extracting redundant facts. Such extractions can hardly be directly used to populate the existing KB. Moreover, many correct extractions are not relevant to the document, which limits the applications to understand the essential information that the document conveys. In this paper, we propose an end-to-end system which takes a target incomplete KB and documents as input. It first performs joint entity and relation linking to the existing KB based on both contexts of document and background KB information. Then it summarizes the extracted facts by considering the relevance to the document and the diversity between them. Extensive experiments over real datasets demonstrate the effectiveness and efficiency of the proposed methods.