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A Knowledge Representation PractionaryThe Precepts

A Knowledge Representation Practionary: The Precepts [Truth, though fallible, exists. Knowledge thus should express a coherent reality, to reflect a logical consistency and structure that comport with our observations about the world. How we represent that reality has syntactic variation and ambiguities of a semantic nature that can only be resolved by context. To deal in the realm of knowledge and belief, the purpose of our KR systems, we must be able to ingest and process electronic data in any form. This information includes any managed by databases, but it can also include messages, documents, Web pages, or data tables. A hub-and-spoke design with a canonical data model is a superior way to organize, manipulate, and manage input information. Our source information embodies conceptual differences and value and attributes differences in raw data. By understanding the sources of semantic heterogeneity, we set the basis for extracting meaning and resolving ambiguities. Once we resolve the source information, we need to organize it into ‘natural’ classes and relate those classes coherently and consistently to one another. This organization takes the form of a knowledge graph. Semantic technologies have the flexibility and openness required for this task. Traditional relational databases do not; they are inflexible and fragile when the nature (schema) of the world changes, and require expensive re-architecting in the face of new knowledge or new relationships. In comparison, knowledge graphs provide natural groupings of concepts and entity types to characterize the domain at hand, situated to one another with testable relations.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Knowledge Representation PractionaryThe Precepts

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
85 –104
DOI
10.1007/978-3-319-98092-8_5
Publisher site
See Chapter on Publisher Site

Abstract

[Truth, though fallible, exists. Knowledge thus should express a coherent reality, to reflect a logical consistency and structure that comport with our observations about the world. How we represent that reality has syntactic variation and ambiguities of a semantic nature that can only be resolved by context. To deal in the realm of knowledge and belief, the purpose of our KR systems, we must be able to ingest and process electronic data in any form. This information includes any managed by databases, but it can also include messages, documents, Web pages, or data tables. A hub-and-spoke design with a canonical data model is a superior way to organize, manipulate, and manage input information. Our source information embodies conceptual differences and value and attributes differences in raw data. By understanding the sources of semantic heterogeneity, we set the basis for extracting meaning and resolving ambiguities. Once we resolve the source information, we need to organize it into ‘natural’ classes and relate those classes coherently and consistently to one another. This organization takes the form of a knowledge graph. Semantic technologies have the flexibility and openness required for this task. Traditional relational databases do not; they are inflexible and fragile when the nature (schema) of the world changes, and require expensive re-architecting in the face of new knowledge or new relationships. In comparison, knowledge graphs provide natural groupings of concepts and entity types to characterize the domain at hand, situated to one another with testable relations.]

Published: Dec 13, 2018

Keywords: Data model; Fallibility; Semantic heterogeneity; Knowledge graph

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