Knowledge Graphs and Semantic Web | Zendy

Anna  Nguyen | Zendy; Tobias  Weller | Zendy; Michael  Färber | Zendy; York  Sure-Vetter | Zendy

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Knowledge Graphs and Semantic Web

Author(s) -

Anna Nguyen,

Tobias Weller,

Michael Färber,

York Sure-Vetter

Publication year - 2020

Publication title -

communications in computer and information science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.16

H-Index - 51

eISSN - 1865-0937

pISSN - 1865-0929

DOI - 10.1007/978-3-030-65384-2

Subject(s) - computer science , semantic web , knowledge representation and reasoning , ontology , semantic web stack , natural language processing , information retrieval , social semantic web , representation (politics) , world wide web , artificial intelligence , semantic web rule language , semantic network , semantic computing , semantic analytics , epistemology , philosophy , politics , political science , law

Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, & Reconnaissance: Mutli-Attribute Task Battery (ISR-MATB), comes from Cognitive Science, where researchers use interdisciplinary methods to understand the mind and cognition. The ISR-MATB is a set of tasks that a cognitive or human agent perform which test visual, auditory, and memory capabilities. An ontology can represent a cognitive agent’s background knowledge of the task it was instructed to perform and act as an interchange format between different Cognitive Agent tasks similar to ISR-MATB. We present several modular patterns for representing ISR-MATB task instructions, as well as a unified diagram that links them together.

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