scholarly journals Application of MCAT questions as a testing tool and evaluation metric for knowledge graph–based reasoning systems

2021 ◽  
Author(s):  
Karamarie Fecho ◽  
James Balhoff ◽  
Chris Bizon ◽  
William E. Byrd ◽  
Sui Hang ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Reasoning Systems ◽  
Testing Tool ◽  
Evaluation Metric

Seq2KG: An End-to-End Neural Model for Domain Agnostic Knowledge Graph (not Text Graph) Construction from Text

2020 ◽  
Author(s):  
Michael Stewart ◽  
Wei Liu
Keyword(s):  
Language Processing ◽  
Neural Model ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Annotation Scheme ◽  
Domain Specific ◽  
Wide Range ◽  
End To End ◽  
True Knowledge ◽  
Evaluation Metric

Knowledge Graph Construction (KGC) from text unlocks information held within unstructured text and is critical to a wide range of downstream applications. General approaches to KGC from text are heavily reliant on the existence of knowledge bases, yet most domains do not even have an external knowledge base readily available. In many situations this results in information loss as a wealth of key information is held within "non-entities". Domain-specific approaches to KGC typically adopt unsupervised pipelines, using carefully crafted linguistic and statistical patterns to extract co-occurred noun phrases as triples, essentially constructing text graphs rather than true knowledge graphs. In this research, for the first time, in the same flavour as Collobert et al.'s seminal work of "Natural language processing (almost) from scratch" in 2011, we propose a Seq2KG model attempting to achieve "Knowledge graph construction (almost) from scratch". An end-to-end Sequence to Knowledge Graph (Seq2KG) neural model jointly learns to generate triples and resolves entity types as a multi-label classification task through deep learning neural networks. In addition, a novel evaluation metric that takes both semantic and structural closeness into account is developed for measuring the performance of triple extraction. We show that our end-to-end Seq2KG model performs on par with a state of the art rule-based system which outperformed other neural models and won the first prize of the first Knowledge Graph Contest in 2019. A new annotation scheme and three high-quality manually annotated datasets are available to help promote this direction of research.

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.

Build A Knowledge Graph

2019 ◽  
Author(s):  
Jemmy Wiratama
Keyword(s):  
Knowledge Graph

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