Year: 2022
Type: conference-paper
Abstract: Commonsense knowledge is essential for the reasoning of AI systems, particularly in the context of action planning for robots. The focus of this paper is on common-sense object properties, which are especially useful to restrict the search space of planning algorithms. Popular sources for such knowledge are commonsense knowledge bases that provide the information in a structured form. However, the utility of the provided object-property pairs is limited as they can be simply incorrect, subjective, unspecific, or relate only to a narrow context. In this paper, we suggest a methodology to create a highly accurate dataset of object properties that are related to common physical attributes. The approach is based on filtering non-physical properties within commonsense knowledge bases and improving the accuracy of the remaining object-property pairs based on supervised machine learning using annotated data. Thereby, we evaluate different types of features and models and significantly increase the correctness of object-property pairs compared to the original sources.
Authors: Viktor Losing, Julian Eggert
Institution: Honda (Germany)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4382201593