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

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.

Publication DOI: https://doi.org/10.1016/j.future.2019.10.002
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 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).
Uncontrolled Keywords: Event extraction,Probabilistic inference,Probabilistic semantic link network,Semantic link network,Software,Hardware and Architecture,Computer Networks and Communications
Publication ISSN: 1872-7115
Full Text Link:
Related URLs: https://linking ... 167739X18322210 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-03-01
Published Online Date: 2019-10-04
Accepted Date: 2019-10-01
Authors: Li, Wei
Zhuge, Hai (ORCID Profile 0000-0001-8250-6408)

Export / Share Citation


Statistics

Additional statistics for this record