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Knowledge Representation Part II III
Jan Pettersen Nytun, Knowledge Representation III, UiA
Credit to
Ref. f.: Preface + Chapter 1 – 2
Knowledge Representation Part II III Credit to Ref. f.: Preface + - - PowerPoint PPT Presentation
Knowledge Representation Part II III Credit to Ref. f.: Preface + Chapter 1 2 1 Jan Pettersen Nytun, Knowledge Representation III, UiA Preface S O P Beyond being a query language, SPARQL is a powerful graph-matching language
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Jan Pettersen Nytun, Knowledge Representation III, UiA
Ref. f.: Preface + Chapter 1 – 2
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…Beyond being a query language, SPARQL is a powerful graph-matching language… SPARQL can be used to specify general inferencing in a concise and precise way. … It turns out to be a lot easier to describe RDF, RDFS, and OWL in terms of SPARQL.
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…having a web site be a collection of data, from which the web page presentations are generated. …focuses not on the presentation but on the subjects of the presentation…. …semantic applications… they explicitly represent the relationships that underlie the application and generate presentations as needed.
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From Wikipedia, the free encyclopedia:
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When describing a set of things, some of them will have some things in common (commonality), and some will have important differences (variability). Managing commonality and variability is a fundamental aspect of modeling in general, and
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The Semantic Web standards also use this idea of class hierarchy… unlike OOP… not focused on software representation, classes are not defined in terms of behaviors of functions.
SSB = solar system body Prefixes: astro = astronomy horo = horoscope IAU = International Astronomical Union
Handling Commonality and Variability with Classes
Each layer comes from a different source. The entire model is the combination of all the layers, viewed as a single, unified whole.
(prefSymbol seems a bit problematic?)
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