SEMANTIC WEB PDF
PDF | The Semantic Web is a mesh of information linked up in such a way as to be easily processable by machines, on a global scale. You can think of it as. PDF | The Semantic Web is an ambitious vision, first proposed by Tim Berners- Lee, to extend today's Web – imbuing it with a sense of meaning. The articulation . Introduction to the Semantic Web. (tutorial). Semantic Technology Conference. San Jose, California, USA. June 15, Ivan Herman, W3C ivan @ppti.info
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accessible via standard Web technologies. ▻ data are interlinked over the Web. ▻ ie, data can be integrated over the Web. ▻ This is where Semantic Web. Semantic Web was coined by Tim Berners Lee, the father of the World Wide Web and the Intro Semantic Web and RDF(S) - A biased introduction (), pdf. Step 1 -- Describe: putting data on the Web in machine-understandable form -- a Semantic Web. • RDF (based on XML). • Master list of terms used in a document.
The basic idea behind the Semantic Web was that everyone would use a new set of standards to annotate their webpages with little bits of XML.
These little bits of XML would have no effect on the presentation of the webpage, but they could be read by software programs to divine meaning that otherwise would only be available to humans. The bits of XML were a way of expressing metadata about the webpage.
We are all familiar with metadata in the context of a file system: When we look at a file on our computers, we can see when it was created, when it was last updated, and whom it was originally created by. Likewise, webpages on the Semantic Web would be able to tell your browser who authored the page and perhaps even where that person went to school, or where that person is currently employed.
In theory, this information would allow Semantic Web browsers to answer queries across a large collection of webpages. In their article for Scientific American, Berners-Lee and his co-authors explain that you could, for example, use the Semantic Web to look up a person you met at a conference whose name you only partially remember.
Cory Doctorow, a blogger and digital rights activist, published an influential essay in that pointed out the many problems with depending on voluntarily supplied metadata. Even if users were universally diligent and well-intentioned, in order for the metadata to be robust and reliable, users would all have to agree on a single representation for each important concept. Doctorow argued that in some cases a single representation might not be appropriate, desirable, or fair to all users.
Munat concludes that a general-purpose Semantic Web is unworkable, and that the focus should be on specific domains within medicine and science.
Others have also seen the Semantic Web project as tragically flawed, though they have located the flaw elsewhere.
In forums like the World Wide Web Consortium W3C , a huge amount of effort and discussion went into creating standards before there were any applications out there to standardize. But that never happened because—as has been discussed on this blog before—the putative benefits of something like XML are not easy to sell to a programmer when the alternatives are both entirely sufficient and much easier to understand.
If the Semantic Web was not an outright impossibility, it was always going to require the contributions of lots of clever people working in concert.
The long effort to build the Semantic Web has been said to consist of four phases. Between and , the W3C issued a slew of new standards laying out the foundational technologies of the Semantic future.
RDF was originally conceived of as a tool for modeling metadata and was partly based on earlier attempts by Ramanathan Guha, an Apple engineer, to develop a metadata system for files stored on Apple computers. RDF would be the grammar in which Semantic webpages expressed information. The grammar is a simple one: Facts about the world are expressed in RDF as triplets of subject, predicate, and object.
This example is in a format called Turtle, which expresses RDF triplets as straightforward sentences terminated by periods. The three essential sentences, which appear above after the prefix preamble, state three facts: Other specifications finalized and drafted during this first era of Semantic Web development describe all the ways in which RDF can be used. RDF Schema and OWL, in other words, are tools for creating what are known as ontologies, explicit specifications of what can and cannot be said within a specific domain.
An ontology might include a rule, for example, expressing that no person can be the mother of another person without also being a parent of that person. The hope was that these ontologies would be widely used not only to check the accuracy of RDF found in the wild but also to make inferences about omitted information. In , Tim Berners-Lee posted a short article in which he argued that the existing work on Semantic Web standards needed to be supplemented by a concerted effort to make semantic data available on the web.
Perhaps the most successful of these datasets was DBpedia , a giant repository of RDF triplets extracted from Wikipedia articles. Today DBpedia describes 4.
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By , JSON had begun its meteoric rise to popularity. It was less verbose and more readable. The approach was a more practical and less abstract one, where immediate applications in search results were the focus. The schema. Two, anyone accessing the URL should get data back.
Three, relationships in the data should point to additional URLs with data. Web 3. Automated reasoning systems will have to deal with all of these issues in order to deliver on the promise of the Semantic Web. Vastness: The World Wide Web contains many billions of pages. The SNOMED CT medical terminology ontology alone contains , class names, and existing technology has not yet been able to eliminate all semantically duplicated terms.
Any automated reasoning system will have to deal with truly huge inputs. Vagueness: These are imprecise concepts like "young" or "tall". This arises from the vagueness of user queries, of concepts represented by content providers, of matching query terms to provider terms and of trying to combine different knowledge bases with overlapping but subtly different concepts.
Fuzzy logic is the most common technique for dealing with vagueness. Uncertainty: These are precise concepts with uncertain values. For example, a patient might present a set of symptoms that correspond to a number of different distinct diagnoses each with a different probability.
Probabilistic reasoning techniques are generally employed to address uncertainty. Inconsistency: These are logical contradictions that will inevitably arise during the development of large ontologies, and when ontologies from separate sources are combined. Deductive reasoning fails catastrophically when faced with inconsistency, because "anything follows from a contradiction".
Defeasible reasoning and paraconsistent reasoning are two techniques that can be employed to deal with inconsistency.
Entity Resolution in the Web of Data. Library Linked Data in the Cloud: Semantic Mining of Social Networks.
Social Semantic Web Mining. Semantic Breakthrough in Drug Discovery. Semantics in Mobile Sensing. Aaron Swartz's A Programmable Web: An Unfinished Work.Bibliomacy ; iu: AdministrativeStaff a rdfs: He defines the Semantic Web as "a web of data that can be processed directly and indirectly by machines".
Automated reasoning systems will have to deal with all of these issues in order to deliver on the promise of the Semantic Web. Sean B.