Statistical Relational Learning (SRL) as an Enabling Technology for Data Acquisition and Data Fusion in Video

2013 ◽  
Author(s):  
Lise Getoor ◽  
David Jacobs
Keyword(s):  
Data Fusion ◽  
Data Acquisition ◽  
Relational Learning ◽  
Statistical Relational Learning ◽  
Enabling Technology

Software Vulnerabilities, Products and Exploits: A Statistical Relational Learning Approach

2021 ◽  
Author(s):  
Caina Figueiredo ◽  
Joao Gabriel Lopes ◽  
Rodrigo Azevedo ◽  
Gerson Zaverucha ◽  
Daniel Sadoc Menasche ◽  
...  
Keyword(s):  
Relational Learning ◽  
Statistical Relational Learning ◽  
Learning Approach ◽  
Software Vulnerabilities

Statistical relational learning based automatic data cleaning

2019 ◽  
Vol 13 (1) ◽  
pp. 215-217 ◽  
Author(s):  
Weibang Li ◽  
Ling Li ◽  
Zhanhuai Li ◽  
Mengtian Cui
Keyword(s):  
Data Cleaning ◽  
Relational Learning ◽  
Statistical Relational Learning ◽  
Automatic Data

scholarly journals Function-Based Search of Place Using Theoretical, Empirical and Probabilistic Patterns

2019 ◽  
Vol 8 (2) ◽  
pp. 92 ◽  
Author(s):  
Emmanuel Papadakis ◽  
George Baryannis ◽  
Andreas Petutschnig ◽  
Thomas Blaschke
Keyword(s):  
Spatial Analysis ◽  
Relational Learning ◽  
Statistical Relational Learning ◽  
Probabilistic Logic ◽  
Use Case ◽  
Empirical Knowledge ◽  
Place Names ◽  
Fine Grained ◽  
Spatial Composition ◽  
Automated Process

Searching for places rather than traditional keyword-based search represents significant challenges. The most prevalent method of addressing place-related queries is based on place names but has limited potential due to the vagueness of natural language and its tendency to lead to ambiguous interpretations. In previous work we proposed a system-oriented logic-based formalization of place that goes beyond place names by introducing composition patterns of place which enable function-based search of space. In this study, we introduce flexibility into these patterns in terms of what is included when describing the spatial composition of a place using two distinct approaches, based on modal and probabilistic logic. Additionally, we propose a novel automated process of extracting these patterns relying on both theoretical and empirical knowledge, using statistical and spatial analysis and statistical relational learning. The proposed methodology is exemplified through the use case of locating all areas within London that support shopping-related functionality. Results show that the newly introduced patterns can identify more relevant areas, additionally offering a more fine-grained representation of the level of support of the required functionality.

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