Common-sense knowledge of physical properties of objects such as size and weight is required in a vast variety of AI applications. Yet, available common-sense knowledge-bases cannot answer simple questions regarding these properties such as “is a microwave oven bigger than a spoon?” or “is a feather heavier than a king size mat-tress?”. To bridge this gap, we harvest semi-structured data associated with physical properties of objects from the web. We then use an unsupervised taxonomy merging scheme to map a set of extracted objects to WordNet hierarchy. We also train a classifier to extend WordNet taxonomy to address both fine-grained and missing concepts. Finally, we use an ensemble of Gaussian mixture models to learn the distribution parameters of these properties. We also propose a Monte Carlo inference mechanism to answer comparative questions. Results suggest that the proposed approach can answer 94.6% of such questions, correctly.
CITATION STYLE
Hassani, K., & Lee, W. S. (2017). Learning physical properties of objects using gaussian mixture models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10233 LNAI, pp. 179–190). Springer Verlag. https://doi.org/10.1007/978-3-319-57351-9_23
Mendeley helps you to discover research relevant for your work.