Probabilistic inference on uncertain semantic link network and its application in event identification

Wei Li; Hai Zhuge

doi:10.1016/j.future.2019.10.002

Probabilistic inference on uncertain semantic link network and its application in event identification

Wei Li, Hai Zhuge

Research output: Contribution to journal › Article › peer-review

Abstract

The Probabilistic Semantic Link Network (P-SLN) is a model for enhancing the ability of Semantic Link Network in representing uncertainty. Probabilistic inference over uncertain semantic links can process the likelihood and consistency of uncertain semantic links. This work develops the P-SLN model by incorporating probabilistic inference rules and consistency constraints. Two probabilistic inference mechanisms are incorporated into the model. The application of probabilistic inference on SLN of events for joint event identification verifies the effectiveness of the proposed model.

Original language	English
Pages (from-to)	32-42
Number of pages	11
Journal	Future Generation Computer Systems
Volume	104
Early online date	4 Oct 2019
DOIs	https://doi.org/10.1016/j.future.2019.10.002
Publication status	Published - 1 Mar 2020

Bibliographical note

© 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Funding: National Science Foundation of China (project no. 61640212, No. 61876048).

Keywords

Event extraction
Probabilistic inference
Probabilistic semantic link network
Semantic link network

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10.1016/j.future.2019.10.002

Probabilistic Inference on Uncertain Semantic Link NetworkAccepted author manuscript, 867 KBLicence: CC BY-NC-ND 3.0

Cite this

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abstract = "The Probabilistic Semantic Link Network (P-SLN) is a model for enhancing the ability of Semantic Link Network in representing uncertainty. Probabilistic inference over uncertain semantic links can process the likelihood and consistency of uncertain semantic links. This work develops the P-SLN model by incorporating probabilistic inference rules and consistency constraints. Two probabilistic inference mechanisms are incorporated into the model. The application of probabilistic inference on SLN of events for joint event identification verifies the effectiveness of the proposed model.",

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AU - Li, Wei

AU - Zhuge, Hai

N1 - © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Funding: National Science Foundation of China (project no. 61640212, No. 61876048).

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AB - The Probabilistic Semantic Link Network (P-SLN) is a model for enhancing the ability of Semantic Link Network in representing uncertainty. Probabilistic inference over uncertain semantic links can process the likelihood and consistency of uncertain semantic links. This work develops the P-SLN model by incorporating probabilistic inference rules and consistency constraints. Two probabilistic inference mechanisms are incorporated into the model. The application of probabilistic inference on SLN of events for joint event identification verifies the effectiveness of the proposed model.

KW - Event extraction

KW - Probabilistic inference

KW - Probabilistic semantic link network

KW - Semantic link network

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SP - 32

EP - 42

JO - Future Generation Computer Systems

JF - Future Generation Computer Systems

ER -

Probabilistic inference on uncertain semantic link network and its application in event identification

Abstract

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Keywords

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