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PSL specifications

Information


Unique identifier OMICS_12422
Name PSL
Alternative name Probabilistic soft logic
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Java
Computer skills Advanced
Version 2.0
Stability Stable
Source code URL https://codeload.github.com/linqs/psl/zip/master
Maintained Yes

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Maintainer


  • person_outline Dhanya Sridhar <>

Publication for Probabilistic soft logic

PSL in publications

 (3)
PMCID: 5940181
PMID: 29738537
DOI: 10.1371/journal.pone.0196865

[…] developed an integrative label propagation framework to model ddis by integration of adrs and chemical structures. sridhar et al. [] developed a probabilistic approach for predicting ddis. they used probabilistic soft logic framework which is highly scalable. the evaluation demonstrated of more than 50% improvement over baselines. ferdousi et al. [] reported on a methodology for ddis modeling […]

PMCID: 5446892
PMID: 28588611
DOI: 10.1155/2017/4092135

[…] each fact independently. more recently, pujara et al. [] improved the model of jiang et al. by including multiple extractors and reasoning about coreferent entities. furthermore, pujara et al. used probabilistic soft logic (psl) to avoid scalability limitation of mlns. dong et al. [] employed supervised machine learning methods for fusing distinct information sources by combining noisy […]

PMCID: 4738730
PMID: 26884747
DOI: 10.1155/2016/2174613

[…] quit the course. yang et al. [] developed a survival model that allows us to measure the influence of factors extracted from learning behavior data on student dropout rate. ramesh et al. [] used probabilistic soft logic (psl) to model student engagement by capturing domain knowledge about student interactions and performance. balakrishnan [] used a combination of students' week 1 assignment […]


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PSL institution(s)
Computer Science Department, University of California Santa Cruz, Santa Cruz, CA, USA; Computer Science Department, University of Maryland, College Park, MD, USA
PSL funding source(s)
This work was supported by the National Science Foundation [IIS1218488].

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