knowledge representation part ii iii
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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


  1. Knowledge Representation Part II III Credit to Ref. f.: Preface + Chapter 1 – 2 1 Jan Pettersen Nytun, Knowledge Representation III, UiA

  2. Preface S O P …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. 2

  3. Semantic Data S O P …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. 3

  4. S The AAA Slogan O P A nyone can say A nything about A ny topic. One of the basic tenets of the Web in general and the Semantic Web in particular. 4

  5. S O Open world/Closed world P From Wikipedia, the free encyclopedia: … the open-world assumption … the truth value of a statement may be true irrespective of whether or not it is known to be true. It is the opposite of the closed-world assumption … any statement that is true is also known to be true. 5

  6. S O P T he AAA slogan implies that there could always be something new that someone will say. I.e., AAA is based on an open world assumption. 6

  7. S Nonunique Naming O P Since the speakers on the Web won’t necessarily coordinate their naming efforts, the same entity could be known by more than one name. 7

  8. S Commonality and Variability O P 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 of Semantic Web models in particular. 8

  9. S O P Handling Commonality and Variability with Classes The Semantic Web standards also use this idea of class hierarchy… unlike OOP… not focused on software representation, classes are not SSB = solar system body defined in terms of Prefixes: astro = astronomy behaviors of horo = horoscope functions. IAU = International Astronomical Union 9

  10. Merging Models 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?) 10

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