About: Probabilistic soft logic

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Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains.It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment.PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge.More specifically, PSL uses "soft" logic as its logical component and Markov random fields as its statistical model.PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the maximum a posteriori (MAP) state).The "softening" of the logical formulas make

Attributes	Values
rdf:type	Thing software schema:CreativeWork work wikidata:Q386724 Software
rdfs:label	Probabilstic soft logic (ja) Probabilistic soft logic (en)
rdfs:comment	Probabilistic soft logic (PSL)は、関係するドメインの中での集合的な、確率的理由づけのためのSRLフレームワーク。PSLは[0,1]の間の値をとるソフト真理変数に関する確率変数のグラフィカルモデルのためのテンプレート言語として一階述語論理を用いる。 (ja) Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains.It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment.PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge.More specifically, PSL uses "soft" logic as its logical component and Markov random fields as its statistical model.PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the maximum a posteriori (MAP) state).The "softening" of the logical formulas make (en)
foaf:name	PSL (en)
foaf:homepage	https://psl.linqs.org
name	PSL (en)
foaf:depiction
dct:subject	Markov networks Bayesian statistics
Wikipage page ID	44132510 (xsd:integer)
Wikipage revision ID	1036442666 (xsd:integer)
Link from a Wikipage to another Wikipage	Probabilistic logic network Python (programming language) Command-line interface Social network Convex function Markov networks Apache Maven Application programming interface Linux MacOS Machine Learning Machine learning Closed-world assumption Windows Fuzzy logic Linear combination Link prediction Lise Getoor Logarithmically convex function First-order logic Pandas (software) Graphical model Journal of Machine Learning Research Java (programming language) Łukasiewicz logic Bayesian statistics Collective classification Record linkage Disjunctive normal form Polynomial time Ontology alignment Markov logic network Markov random field Maximum a posteriori estimation Statistical relational learning NP-hardness Python Package Index Apache License 2.0 Markov random fields Lukasiewicz fuzzy logic
Link from a Wikipage to an external page	https://linqs.soe.ucsc.edu/ https://psl.linqs.org https://github.com/linqs/psl https://www.youtube.com/channel/UCJjzqRLiAIa3qENUkzK0zMA
sameAs	Probabilistic soft logic Probabilistic soft logic Probabilistic soft logic Probabilistic soft logic Probabilistic soft logic
dbp:wikiPageUsesTemplate	dbt:Infobox_software dbt:Reflist dbt:Start_date
thumbnail	wiki-commons:Special:FilePath/PSL_Logo.png?width=300
developer	https://linqs.soe.ucsc.edu/
genre	Machine Learning Statistical relational learning
latest release date	2020-05-20 (xsd:date)
latest release version	2.200000 (xsd:double)
license	Apache License 2.0
logo	PSL_Logo.png (en)
platform	Linux MacOS Windows
programming language	Java (programming language)
released	2011-09-23 (xsd:date)
repo	https://github.com/linqs/psl
website	https://psl.linqs.org
has abstract	Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains.It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment.PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge.More specifically, PSL uses "soft" logic as its logical component and Markov random fields as its statistical model.PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the maximum a posteriori (MAP) state).The "softening" of the logical formulas makes inference a polynomial time operation rather than an NP-hard operation. (en) Probabilistic soft logic (PSL)は、関係するドメインの中での集合的な、確率的理由づけのためのSRLフレームワーク。PSLは[0,1]の間の値をとるソフト真理変数に関する確率変数のグラフィカルモデルのためのテンプレート言語として一階述語論理を用いる。 (ja)
gold:hypernym	Framework
prov:wasDerivedFrom	wikipedia-en:Probabilistic_soft_logic?oldid=1036442666&ns=0
page length (characters) of wiki page	16635 (xsd:nonNegativeInteger)
latest release date	2020-05-20 (xsd:date)
latest release version	2.2.2
release date	2011-09-23 (xsd:date)
computing platform	Linux MacOS Windows
developer	https://linqs.soe.ucsc.edu/
genre	Machine Learning Statistical relational learning
license	Apache License 2.0

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