scholarly journals Knowledge-Graph-Based Semantic Labeling of Tabular Data

2020 ◽  
Author(s):  
Ahmad Alobaid
Semantic Web ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 5-20
Author(s):  
Ahmad Alobaid ◽  
Emilia Kacprzak ◽  
Oscar Corcho

A lot of tabular data are being published on the Web. Semantic labeling of such data may help in their understanding and exploitation. However, many challenges need to be addressed to do this automatically. With numbers, it can be even harder due to the possible difference in measurement accuracy, rounding errors, and even the frequency of their appearance. Multiple approaches have been proposed in the literature to tackle the problem of semantic labeling of numeric values in existing tabular datasets. However, they also suffer from several shortcomings: closely coupled with entity-linking, rely on table context, need to profile the knowledge graph, and require manual training of the model. Above all, however, they all treat different types of numeric values evenly. In this paper, we tackle these problems and validate our hypothesis: whether taking into account the typology of numeric data in semantic labeling yields better results.


Semantic Web ◽  
2021 ◽  
pp. 1-23
Author(s):  
Steven J. Baskauf ◽  
Jessica K. Baskauf

The W3C Generating RDF from Tabular Data on the Web Recommendation provides a mechanism for mapping CSV-formatted data to any RDF graph model. Since the Wikibase data model used by Wikidata can be expressed as RDF, this Recommendation can be used to document tabular snapshots of parts of the Wikidata knowledge graph in a simple form that is easy for humans and applications to read. Those snapshots can be used to document how subgraphs of Wikidata have changed over time and can be compared with the current state of Wikidata using its Query Service to detect vandalism and value added through community contributions.


Author(s):  
Ernesto Jiménez-Ruiz ◽  
Oktie Hassanzadeh ◽  
Vasilis Efthymiou ◽  
Jiaoyan Chen ◽  
Kavitha Srinivas

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao

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.


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

I'm an Science & Technology enthusiast. I still learn how to build a knowledge graph and how to write a paper.


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