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Extraction of Common Physical Properties of Everyday Objects from Structured Sources

Published: 27 June 2023 Publication History

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.

References

[1]
Michael Beetz, Daniel Beßler, Andrei Haidu, Mihai Pomarlan, Asil Kaan Bozcuoğlu, and Georg Bartels. 2018. Know rob 2.0—a 2nd generation knowledge processing framework for cognition-enabled robotic agents. In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 512–519.
[2]
Christopher J Conti, Aparna S Varde, and Weitian Wang. 2020. Robot action planning by commonsense knowledge in human-robot collaborative tasks. In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).
[3]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. https://doi.org/10.18653/v1/N19-1423
[4]
Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020. Cosmic: Commonsense knowledge for emotion identification in conversations. arXiv preprint arXiv:2010.02795 (2020).
[5]
Jian Guan, Yansen Wang, and Minlie Huang. 2019. Story ending generation with incremental encoding and commonsense knowledge. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 6473–6480.
[6]
Andrey Konstantinov, Vadim Moshkin, and Nadezhda Yarushkina. 2020. Approach to the use of language models BERT and Word2vec in sentiment analysis of social network texts. In International Scientific and Practical Conference in Control Engineering and Decision Making. Springer, 462–473.
[7]
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In European conference on computer vision. Springer, 740–755.
[8]
Cynthia Matuszek, Michael Witbrock, John Cabral, and John DeOliveira. 2006. An introduction to the syntax and content of Cyc. UMBC Computer Science and Electrical Engineering Department Collection (2006).
[9]
John McCarthy 1960. Programs with common sense. RLE and MIT computation center Cambridge, MA, USA.
[10]
Tomas Mikolov, Kai Chen, Greg S. Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. http://arxiv.org/abs/1301.3781
[11]
George A Miller. 1998. WordNet: An electronic lexical database. MIT press.
[12]
Tiago Mota and Mohan Sridharan. 2019. Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning on Robots. In Robotics: science and systems.
[13]
Tuan-Phong Nguyen, Simon Razniewski, Julien Romero, and Gerhard Weikum. 2021. Refined commonsense knowledge from large-scale web contents. arXiv preprint arXiv:2112.04596 (2021).
[14]
Julien Romero, Simon Razniewski, Koninika Pal, Jeff Z. Pan, Archit Sakhadeo, and Gerhard Weikum. 2019. Commonsense properties from query logs and question answering forums. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1411–1420.
[15]
Edward H Shortliffe, Bruce G Buchanan, and Edward A Feigenbaum. 1979. Knowledge engineering for medical decision making: A review of computer-based clinical decision aids. Proc. IEEE 67, 9 (1979), 1207–1224.
[16]
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. Conceptnet 5.5: An open multilingual graph of general knowledge. In Thirty-first AAAI conference on artificial intelligence.
[17]
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2018. Commonsenseqa: A question answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937 (2018).
[18]
Niket Tandon, Gerard De Melo, and Gerhard Weikum. 2017. Webchild 2.0: Fine-grained commonsense knowledge distillation. In Proceedings of ACL 2017, System Demonstrations. 115–120.
[19]
Hongming Zhang, Daniel Khashabi, Yangqiu Song, and Dan Roth. 2020. TransOMCS: From Linguistic Graphs to Commonsense Knowledge. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) 2020.
[20]
Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2019. Improving question answering by commonsense-based pre-training. In CCF International Conference on Natural Language Processing and Chinese Computing.

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  • (2023)Situational Question Answering over Commonsense Knowledge Using Memory NetsKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-031-43471-6_8(175-194)Online publication date: 16-Sep-2023

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NLPIR '22: Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval
December 2022
241 pages
ISBN:9781450397629
DOI:10.1145/3582768
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 27 June 2023

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  1. knowledge bases
  2. neural networks
  3. transformer model

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  • (2023)Situational Question Answering over Commonsense Knowledge Using Memory NetsKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-031-43471-6_8(175-194)Online publication date: 16-Sep-2023

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