Reasoning Under Uncertainty in Knowledge Graphs

2020 ◽  
pp. 131-139
Author(s):  
Luigi Bellomarini ◽  
Eleonora Laurenza ◽  
Emanuel Sallinger ◽  
Evgeny Sherkhonov
Keyword(s):  
Reasoning Under Uncertainty ◽  
Knowledge Graphs

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

Reasoning Under Uncertainty.

1995 ◽  
Author(s):  
Joseph Y. Halpern
Keyword(s):  
Reasoning Under Uncertainty

scholarly journals GREASE: A Generative Model for Relevance Search over Knowledge Graphs

2020 ◽  
Author(s):  
Tianshuo Zhou ◽  
Ziyang Li ◽  
Gong Cheng ◽  
Jun Wang ◽  
Yu'Ang Wei
Keyword(s):  
Generative Model ◽  
Knowledge Graphs

scholarly journals Industry-scale knowledge graphs

2019 ◽  
Vol 62 (8) ◽  
pp. 36-43 ◽  
Author(s):  
Natasha Noy ◽  
Yuqing Gao ◽  
Anshu Jain ◽  
Anant Narayanan ◽  
Alan Patterson ◽  
...  
Keyword(s):  
Knowledge Graphs

Building knowledge graphs of homicide investigation chronologies

2020 ◽  
Author(s):  
Ritika Pandey ◽  
P. Jeffrey Brantingham ◽  
Craig D. Uchida ◽  
George Mohler
Keyword(s):  
Knowledge Graphs ◽  
Homicide Investigation

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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