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.
|Number of pages||11|
|Journal||Future Generation Computer Systems|
|Early online date||4 Oct 2019|
|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).
- Event extraction
- Probabilistic inference
- Probabilistic semantic link network
- Semantic link network