Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey

Yuan Gao; Muhammad Rafi

doi:10.1109/SKG49510.2019.00022

Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey

Yuan Gao, Muhammad Rafi

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference publication

Abstract

Modeling the real world is a basic but vital task in computer science. Graphics, uncertainty, and semantics are three key aspects of understanding structure and "why" when handling complex relationships with imperfect or unknown information. The combination of the three aspects can provide a systematic and effective method for modeling the real world. This paper presents a survey of the efforts towards combining these aspects. One branch of the efforts is to combine graphics and uncertainty as probabilistic graphical models (PGMs), and then associate PGMs with semantics. The other branch is to combine graphics and semantics as graph-based knowledge representations, and then add the probability to handle uncertainty. We introduce the models and methods involved in these efforts and discuss the expressiveness, pros and cons of them. Finally, we suggest future work in this domain.

Original language	English
Title of host publication	Proceedings - 15th International Conference on Semantics, Knowledge and Grids
Subtitle of host publication	On Big Data, AI and Future Interconnection Environment, SKG 2019
Editors	Hai Zhuge, Xiaoping Sun
Publisher	IEEE
Pages	81-88
Number of pages	8
ISBN (Electronic)	978-1-7281-5823-5
ISBN (Print)	978-1-7281-5824-2
DOIs	https://doi.org/10.1109/SKG49510.2019.00022
Publication status	Published - 23 Mar 2020
Event	2019 15th International Conference on Semantics, Knowledge and Grids (SKG) - Guangzhou, China Duration: 17 Sep 2019 → 18 Sep 2019

Publication series

Name	Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019

Conference

Conference	2019 15th International Conference on Semantics, Knowledge and Grids (SKG)
Period	17/09/19 → 18/09/19

Keywords

Graph-based knowledge representation
Markov logic network
Probabilistic graphical model
Probabilistic soft logic
Semantic link network
Semantic network

Access to Document

10.1109/SKG49510.2019.00022

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Gao, Y., & Rafi, M. (2020). Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey. In H. Zhuge, & X. Sun (Eds.), Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019 (pp. 81-88). [9044080] (Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019). IEEE. https://doi.org/10.1109/SKG49510.2019.00022

Gao, Yuan ; Rafi, Muhammad. / Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey. Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019. editor / Hai Zhuge ; Xiaoping Sun. IEEE, 2020. pp. 81-88 (Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019).

@inproceedings{01965d50e4954a119efb5faa57cfa02a,

title = "Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey",

abstract = "Modeling the real world is a basic but vital task in computer science. Graphics, uncertainty, and semantics are three key aspects of understanding structure and {"}why{"} when handling complex relationships with imperfect or unknown information. The combination of the three aspects can provide a systematic and effective method for modeling the real world. This paper presents a survey of the efforts towards combining these aspects. One branch of the efforts is to combine graphics and uncertainty as probabilistic graphical models (PGMs), and then associate PGMs with semantics. The other branch is to combine graphics and semantics as graph-based knowledge representations, and then add the probability to handle uncertainty. We introduce the models and methods involved in these efforts and discuss the expressiveness, pros and cons of them. Finally, we suggest future work in this domain.",

keywords = "Graph-based knowledge representation, Markov logic network, Probabilistic graphical model, Probabilistic soft logic, Semantic link network, Semantic network",

author = "Yuan Gao and Muhammad Rafi",

year = "2020",

month = mar,

day = "23",

doi = "10.1109/SKG49510.2019.00022",

language = "English",

isbn = "978-1-7281-5824-2",

series = "Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019",

publisher = "IEEE",

pages = "81--88",

editor = "Hai Zhuge and Xiaoping Sun",

booktitle = "Proceedings - 15th International Conference on Semantics, Knowledge and Grids",

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Gao, Y & Rafi, M 2020, Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey. in H Zhuge & X Sun (eds), Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019., 9044080, Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019, IEEE, pp. 81-88, 2019 15th International Conference on Semantics, Knowledge and Grids (SKG), 17/09/19. https://doi.org/10.1109/SKG49510.2019.00022

Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey. / Gao, Yuan; Rafi, Muhammad.

Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019. ed. / Hai Zhuge; Xiaoping Sun. IEEE, 2020. p. 81-88 9044080 (Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference publication

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N2 - Modeling the real world is a basic but vital task in computer science. Graphics, uncertainty, and semantics are three key aspects of understanding structure and "why" when handling complex relationships with imperfect or unknown information. The combination of the three aspects can provide a systematic and effective method for modeling the real world. This paper presents a survey of the efforts towards combining these aspects. One branch of the efforts is to combine graphics and uncertainty as probabilistic graphical models (PGMs), and then associate PGMs with semantics. The other branch is to combine graphics and semantics as graph-based knowledge representations, and then add the probability to handle uncertainty. We introduce the models and methods involved in these efforts and discuss the expressiveness, pros and cons of them. Finally, we suggest future work in this domain.

AB - Modeling the real world is a basic but vital task in computer science. Graphics, uncertainty, and semantics are three key aspects of understanding structure and "why" when handling complex relationships with imperfect or unknown information. The combination of the three aspects can provide a systematic and effective method for modeling the real world. This paper presents a survey of the efforts towards combining these aspects. One branch of the efforts is to combine graphics and uncertainty as probabilistic graphical models (PGMs), and then associate PGMs with semantics. The other branch is to combine graphics and semantics as graph-based knowledge representations, and then add the probability to handle uncertainty. We introduce the models and methods involved in these efforts and discuss the expressiveness, pros and cons of them. Finally, we suggest future work in this domain.

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KW - Semantic network

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Gao Y, Rafi M. Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey. In Zhuge H, Sun X, editors, Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019. IEEE. 2020. p. 81-88. 9044080. (Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019). https://doi.org/10.1109/SKG49510.2019.00022