RuKBC-QA: A Framework for Question Answering over Incomplete KBs Enhanced with Rules Injection

Abstract

The incompleteness of the knowledge base (KB) is one of the key issues when answering natural language questions over an incomplete knowledge base (KB-QA). To alleviate this problem, a framework, RuKBC-QA, is proposed to integrate methods of rule-based knowledge base completion (KBC) into general QA systems. Three main components are included in our framework, namely, a rule miner that mines logic rules from the KB, a rule selector that selects meaningful rules for QA, and a QA model that aggregates information from the original knowledge base and the selected rules. Experiments on WEBQUESTIONS dataset indicate that the proposed framework can effectively alleviate issues caused by incompleteness and obtains a significant improvement in terms of micro average Fl score by 2.4% to 4.5% under different incompleteness settings.