Semantic role-based representations in text classification

2016 ◽  
Author(s):  
Roberta A. Sinoara ◽  
Rafael G. Rossi ◽  
Solange O. Rezende
Keyword(s):  
Text Classification ◽  
Semantic Role ◽  
Role Based

A Semantic Role-Based Approach for Ontology Learning from Spanish Texts

2011 ◽  
pp. 273-280 ◽  
Author(s):  
José Luis Ochoa ◽  
Maria Luisa Hernández-Alcaraz ◽  
Rafael Valencia-García ◽  
Rodrigo Martínez-Béjar
Keyword(s):  
Semantic Role ◽  
Ontology Learning ◽  
Role Based

scholarly journals Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4253
Author(s):  
Rubina Ghazal ◽  
Ahmad Kamran Malik ◽  
Basit Raza ◽  
Nauman Qadeer ◽  
Nafees Qamar ◽  
...  
Keyword(s):  
Access Control ◽  
Intelligent Agents ◽  
Smart Cities ◽  
Role Based Access Control ◽  
Semantic Role ◽  
Role Mining ◽  
Occupational Classification ◽  
Business Environments ◽  
Intelligent Software ◽  
Role Based

Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles.

Dual systems for all: Higher-order, role-based relational reasoning as a uniquely derived feature of human cognition

2019 ◽  
Vol 42 ◽  
Author(s):  
Daniel J. Povinelli ◽  
Gabrielle C. Glorioso ◽  
Shannon L. Kuznar ◽  
Mateja Pavlic
Keyword(s):  
Temporal Reasoning ◽  
Higher Order ◽  
Important Case ◽  
Human Cognition ◽  
Relational Reasoning ◽  
Dual Systems ◽  
First Order ◽  
Reasoning System ◽  
Role Based

Abstract Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order (perceptually-based) relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.

A Role-Based Relational Approach to Examining Injustice in Teams

2012 ◽  
Author(s):  
Jessica L. Siegel ◽  
Michael S. Christian ◽  
Adela S. Garza ◽  
Aleksander P. J. Ellis
Keyword(s):  
Relational Approach ◽  
Role Based
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