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

Wei Li, Hai Zhuge

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)32-42
Number of pages11
JournalFuture Generation Computer Systems
Volume104
Early online date4 Oct 2019
DOIs
Publication statusPublished - 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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