Event-Object Reasoning with Curated Knowledge Bases: Deriving Missing Information
This work addresses the challenge of incomplete data in knowledge bases for researchers and practitioners in AI and knowledge representation, though it appears incremental as it builds on existing reasoning methods.
The paper tackles the problem of missing information in knowledge bases, such as details about sub-events or event components, by providing a formal definition for recovering this information and implementing it using Answer Set Programming (ASP). The result is a framework that enhances reasoning capabilities for answering why and how questions based on curated knowledge bases.
The broader goal of our research is to formulate answers to why and how questions with respect to knowledge bases, such as AURA. One issue we face when reasoning with many available knowledge bases is that at times needed information is missing. Examples of this include partially missing information about next sub-event, first sub-event, last sub-event, result of an event, input to an event, destination of an event, and raw material involved in an event. In many cases one can recover part of the missing knowledge through reasoning. In this paper we give a formal definition about how such missing information can be recovered and then give an ASP implementation of it. We then discuss the implication of this with respect to answering why and how questions.