The cloud of data is a beautiful vision, but it is predicated on the idea that you can make sense of other peoples’ data. Through the use of semantic programming techniques, developers can now pack data and metadata together to make their data more portable and extensible. These techniques not only provide benefits for other who may need to access your data, but generally simplify your own development and data modeling efforts.
This tutorial will introduce participants to Semantic Technologies through a simple examples, demonstrations and code snippets to help participants understand how to build functional semantic applications.
The tutorial does not presuppose any experience with Semantic Technologies and is aimed at developers who are comfortable making REST API calls in the language of the choice. The examples presented will primarily use Python, but should generalize to any modern programming language.
1. Introduction to Semantic Technologies
2. Working with Data
3. Semantic Applications
While developing an Internet laboratory for studying economic equilibria, Jamie started one of the first ISPs in San Francisco so he could get a better connection at home.
He finally got a real job as CTO at DETERMINE Software (now a part of Selectica) helping create order in the unstructured world of Enterprise contract management.
He is now helping to organize the world’s structured information at Metaweb where he oversees data operations.
Toby Segaran is the author of the O’Reilly title, “Programming Collective Intelligence”, Amazon’s top-selling AI book. He frequently speaks on the subjects of machine learning, collective intelligence and freedom of data at conferences worldwide.
He currently holds the title of Data Magnate at Metaweb Technologies, where he works on large-scale data reconciliation problems. Prior to Metaweb he founded Incellico, a biotechnology software company, which was acquired in 2003.
Toby holds a B.Sc in Computer Science from MIT and is deemed a “Person of Exceptional Ability” by the USCIS. He loves applying data-analysis algorithms to everything ranging from pharmaceutical trials to online dating to financial risk models.
Colin combines machine learning and semantic analysis into a deadly one-two punch against information entropy and noisy data. The results of his efforts appear as millions of facts in Freebase.
Prior to joining Metaweb, Colin helped users organize their world through his work on the IRIS semantic desktop project at SRI International.
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