Semantic web. Semantic technologies. Metaweb to Google’s Knowledge Graph: An Interview with John Giannandrea. Alexis Madrigal of The Atlantic recently shared an interview John Giannandrea regarding Google’s Knowledge Graph. Madrigal writes, “The ugly truth is that computers don’t know anything. They have no common sense. This idea had been circulating in Metaweb co-founder John Giannandrea’s head since 1997 when he was working at Netscape and thinking about how to reveal what you did not know you didn’t know on the web. If you were looking at search results for a hiking trail, say, what other hiking trails might you look at? Giannandrea called it ‘going sideways through the web,’ and he loved the idea, even if he couldn’t execute it back then.” John Giannandrea He continues, “Years later, in 2005, Giannandrea teamed up with Danny Hillis and Robert Cook to cofound Metaweb, which had a simple premise: ‘What if we could make a catalog of all the stuff our computer should know?’
Read more here. Image: Courtesy Google. Semanticweb.com - The Voice of Semantic Web Business. How Google Organizes the World: Q&A With the Manager of Knowledge Graph. In May, Google launched a major overhaul of its search results. The Knowledge Graph on the right-hand side of the page displays facts and images about the subject of your query alongside the usual Web results. Google is moving away from basic keyword matching and toward recognizing real-world things and their relationships. We sat down with Emily Moxley, Google’s lead product manager for the Knowledge Graph, to learn how Google is tackling this challenge. ReadWriteWeb: What is Google’s goal with the Knowledge Graph? Emily Moxley: It’s about mapping the real world into something that computers can understand.
RWW: How did you come to work on the Knowledge Graph? EM: I’ve been here for three years, and I started off working on user experience, running lots of experiments and understanding how users scan the page and how they really use search. RWW: Is the Knowledge Graph idea as recent as last summer? EM: It’s building on stuff that’s been developed for quite some time. Ontologie (informatique)
Un article de Wikipédia, l'encyclopédie libre. Par analogie, le terme est repris en informatique et en science de l'information, où une ontologie est l'ensemble structuré des termes et concepts représentant le sens d'un champ d'informations, que ce soit par les métadonnées d'un espace de noms, ou les éléments d'un domaine de connaissances. L'ontologie constitue en soi un modèle de données représentatif d'un ensemble de concepts dans un domaine, ainsi que des relations entre ces concepts. Elle est employée pour raisonner à propos des objets du domaine concerné. Plus simplement, on peut aussi dire que l' « ontologie est aux données ce que la grammaire est au langage ». L'objectif premier d'une ontologie est de modéliser un ensemble de connaissances dans un domaine donné, qui peut être réel ou imaginaire.
Les ontologies informatiques sont des outils qui permettent précisément de représenter un corpus de connaissances sous une forme utilisable par un ordinateur. Notes. Presentation: SPARQL, Queries, & Linked Data. A new presentation from the ICWE Conference is available online. The presentation is titled An Introduction to SPARQL and Queries over Linked Data: “Nowadays, more and more datasets are published on the Web adhering to the Linked Data principles. The availability of this data, including the existence of data-level connections between datasets, presents exciting opportunities for the next generation of Web-based applications.
As a consequence, consuming Linked Data is a highly relevant topic in the context of Web engineering. Our introductory tutorial aims to provide participants with an understanding of one of the basic aspects of Linked Data consumption, that is, querying Linked Data.” The description continues, “The tutorial consists of three main parts: First, we briefly introduce the concept of Linked Data and its underlying data model, the resource description framework (RDF). Download the full presentation here. Image: Courtesy ICWE. Introduction to: RDF vs XML. There has always been a misconception between the relationship of RDF and XML. The main difference: XML is a syntax while RDF is a data model. RDF has several syntaxes (Turtle, N3, etc) and XML is one of those (known as RDF/XML). Actually, RDF/XML is the only W3C standard syntax for RDF (Currently, there is Last Call on Turtle, a new W3C standard syntax for RDF).
Therefore, comparing XML and RDF is like comparing apples with oranges. What can be compared is their data models. Comparing RDF with XML Joshua Tauberer has an excellent comparison between RDF and XML, which I recommend. Flexibility of the Data Model There are different ways of representing data in XML. <product> <title>iPhone</title> <price>$200</price> </product> Another valid XML could be: <product title=”iPhone”> <price>$200</price> </product> Modeling this same data in RDF would only have one way of representing it: ex:product1 rdf:type ex:Product . ex:product1 ex:title “iPhone” . ex:product1 ex:price “200″ .
Summary.