Great video on youtube visualising the relation between
RDFs
URLs
URNs
URIs
URL locates, URN names, URI identifies (both locates and names), and that's something you can find out when you look at how they are named. Unified Resource Identifier (URI) Unified Resource Name (URN) Unified Resource Locator (URL).
The URIs are the basic modules to create RDFs - Resource Description Frameworks.
Each RDF is created from a Subject - Predicat - Object relation such as
"Jessica sells books"
Subject Predicate Object
Following Peter's presentation further we get to the point, where I doubt that we can talk about making computers understand ontology. What we actually do in trying to make computers "understand ontology" is to brake complex semantics into bite sized triplets which together will not leave any space for interpretation. The computer is forced to use the yes/no or binary logic to come to unique conclusions. RDF is dumbing down complex logic into a set of triplets.
Interestingly, Peter makes a mistake that should lead to a wrong response in his slide about trust. In explaining the simple question, "Who is the President of the United States?", he takes the usual approach of the US American, forgetting that there might be a few more clues missing than explained in the longer statement, "Who is the President of the United States according to the latest trustworthy data I have?"Of course it should be "Who is the President of the United States of America according to the latest trustworthy data I have". Humans know that United States is a short form for United States of America. While this may sound like splitting hair, the crux with explaining things to computers is that you have to be as precise as possible to leave out space for alternatives. The space for ambiguity is what really creates the big problem.
Another problem I see is that one and the same sentence in English can have a set of different meanings. While we set the menaings apart by emphasising/stressing different parts of the sentence, depending on what we want to express, computers see the exact same sentence. A precarious example could be "Peter helped his grandfather to get off." While "Getting Off" has different meanings, the sentence doesn't give any further indication how Peter helped his grandfather to get off and also, to get off what?
Further considering the globality of the Web, how will RDF help to mediate between different languages? Is the Subject Predicate Object triplet omni-present in any language across the globe?
Also, what is commonly referred to as "big data" is stored in relational databases. Facebook, Google and a large number of social media outlets already know what you want by following your browser activity on the World Wide Web or in their confined spaces. Meaning is not added by triplets, but by analysing online behaviour. I regard this as a separate ontology about the consumer. While RDF provides semantics about things, big data provides semantics about humans.
RDFs
URLs
URNs
URIs
URL locates, URN names, URI identifies (both locates and names), and that's something you can find out when you look at how they are named. Unified Resource Identifier (URI) Unified Resource Name (URN) Unified Resource Locator (URL).
The URIs are the basic modules to create RDFs - Resource Description Frameworks.
Each RDF is created from a Subject - Predicat - Object relation such as
"Jessica sells books"
Subject Predicate Object
Following Peter's presentation further we get to the point, where I doubt that we can talk about making computers understand ontology. What we actually do in trying to make computers "understand ontology" is to brake complex semantics into bite sized triplets which together will not leave any space for interpretation. The computer is forced to use the yes/no or binary logic to come to unique conclusions. RDF is dumbing down complex logic into a set of triplets.
Interestingly, Peter makes a mistake that should lead to a wrong response in his slide about trust. In explaining the simple question, "Who is the President of the United States?", he takes the usual approach of the US American, forgetting that there might be a few more clues missing than explained in the longer statement, "Who is the President of the United States according to the latest trustworthy data I have?"Of course it should be "Who is the President of the United States of America according to the latest trustworthy data I have". Humans know that United States is a short form for United States of America. While this may sound like splitting hair, the crux with explaining things to computers is that you have to be as precise as possible to leave out space for alternatives. The space for ambiguity is what really creates the big problem.
Another problem I see is that one and the same sentence in English can have a set of different meanings. While we set the menaings apart by emphasising/stressing different parts of the sentence, depending on what we want to express, computers see the exact same sentence. A precarious example could be "Peter helped his grandfather to get off." While "Getting Off" has different meanings, the sentence doesn't give any further indication how Peter helped his grandfather to get off and also, to get off what?
Further considering the globality of the Web, how will RDF help to mediate between different languages? Is the Subject Predicate Object triplet omni-present in any language across the globe?
Also, what is commonly referred to as "big data" is stored in relational databases. Facebook, Google and a large number of social media outlets already know what you want by following your browser activity on the World Wide Web or in their confined spaces. Meaning is not added by triplets, but by analysing online behaviour. I regard this as a separate ontology about the consumer. While RDF provides semantics about things, big data provides semantics about humans.
