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A Knowledge Representation PractionaryKR Vocabulary and Languages

A Knowledge Representation Practionary: KR Vocabulary and Languages [Knowledge representation involves a trade-off in expressivity and practicality. Knowledge graphs and knowledge bases need to be comprehensive for their applicable domains of use, populated with ‘vivid’ knowledge. Specifying all knowledge interactions is neither feasible nor computationally tractable. We use formalisms and logic to infer many relationships and to entail implications. We use deductive reasoning to infer hierarchical relationships, create forward and backward chains, check if domains and ranges are consistent for assertions, assemble attributes applicable to classes based on member attributes, conform with transitivity and cardinality assertions, and check virtually all statements of fact within a knowledge base. As inductive and abductive reasoners become available, they will expand this list of capabilities to include question answering, hypothesis generation and testing, forecasting, decision-making, and real-time systems in robotics. Abductive reasoning must be pragmatic to screen among the many combinatorial options; Peirce advocates paths for identifying qualifying candidates based on plausibility, economy, and potential impact. We want a knowledge representation (KR) language that can model and capture intensional and extensional relations, one that potentially embraces all three kinds of inferential logic; one that is decidable; one that is compatible with a design reflective of particulars and generals; and one that is open world in keeping with the nature of knowledge. Our choice for the knowledge graph is the W3C standard of OWL 2 (the Web Ontology Language). Our choice for data representation and exchange is RDF (Resource Description Framework).] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Knowledge Representation PractionaryKR Vocabulary and Languages

Bergman, Michael K.
Springer Journals — Dec 13, 2018
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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2018
ISBN
978-3-319-98091-1
Pages
151 –180
DOI
10.1007/978-3-319-98092-8_8
Publisher site
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Abstract

[Knowledge representation involves a trade-off in expressivity and practicality. Knowledge graphs and knowledge bases need to be comprehensive for their applicable domains of use, populated with ‘vivid’ knowledge. Specifying all knowledge interactions is neither feasible nor computationally tractable. We use formalisms and logic to infer many relationships and to entail implications. We use deductive reasoning to infer hierarchical relationships, create forward and backward chains, check if domains and ranges are consistent for assertions, assemble attributes applicable to classes based on member attributes, conform with transitivity and cardinality assertions, and check virtually all statements of fact within a knowledge base. As inductive and abductive reasoners become available, they will expand this list of capabilities to include question answering, hypothesis generation and testing, forecasting, decision-making, and real-time systems in robotics. Abductive reasoning must be pragmatic to screen among the many combinatorial options; Peirce advocates paths for identifying qualifying candidates based on plausibility, economy, and potential impact. We want a knowledge representation (KR) language that can model and capture intensional and extensional relations, one that potentially embraces all three kinds of inferential logic; one that is decidable; one that is compatible with a design reflective of particulars and generals; and one that is open world in keeping with the nature of knowledge. Our choice for the knowledge graph is the W3C standard of OWL 2 (the Web Ontology Language). Our choice for data representation and exchange is RDF (Resource Description Framework).]

Published: Dec 13, 2018

Keywords: RDF; OWL; Vocabulary

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