@inproceedings {pub5192,
	title = {Extraction of Common Physical Properties of Everyday Objects from Structured Sources},
	author = {Viktor Losing AND Julian Eggert},
	year = {2022},
	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, unspecifi c, 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 diff erent types of features and models and significantly increase the correctness of object-property pairs compared to the original sources.},
	publisher = {ACM},
	booktitle = {NLPIR {\textquoteright}22: Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval},
	editor = {ICPS / ACM},
	city = {New York},
	pages = {164-168},
	series = {NLPIR Proceedings}
}