A new article reports that the Hasso Plattner Institute will be launching a free online course on Semantic Web Technologies which should begin on May 26, 2014. According to the article, “Anyone wishing to keep up with the current university knowledge on information technology will again have the opportunity in the coming year with the five free online courses to be offered by Hasso Plattner Institute (HPI). The new courses listed in the just released openHPI overview for 2014 are: Concepts in Parallel Computing, Networking via the Internet Protocol TCP/IP, Semantic Web Technologies, In-Memory Data Management and Introduction to Internet Security. Read more
That is the vision of Bart van Leeuwen, Amsterdam Firefighter and founder of software company, Netage. We’ve covered Bart’s work before here at SemanticWeb.com and at the Semantic Technology & Business Conference, and today, there is news that the work is advancing to a new stage.
In the Netherlands, there exist 25 “Safety Regions” (pictured on the left). These organizations coordinate disaster management, fire services, and emergency medical teams. The regions are designed to enable various first responders to work together to deal with complex and severe crises and disasters.
Additionally, the Dutch Police acts as a primary partner organization in these efforts. The police is a national organization, separate from the safety regions and divided into its own ten regions. Read more
It’s getting to be that time again – yup, school days are getting into full swing. Education, of course, is going through a lot of change these days. The Common Core State Standards initiative is changing what students must learn and what teachers must teach in the early grades, while school-specific online courses now are joined by massive online learning courses (MOOCs) that are bringing new learning experiences on a large-scale to everyone from high school and college students to adults who haven’t taken courses inside a live classroom for decades.
It’s under these circumstances that startup Cognii is hoping to make its mark by applying natural language processing and semantic technology to automate assessments for online learning for students and to grade essays for educators. Its initial focus is on the online education sector, though founder and CTO Dharmendra Kanejiya – whose background involves developing algorithms to improve speech recognition at Vlingo, which were applied to Nuance Communications’ solutions when it acquired the company – says it also can have applicability in the real-world classroom.
Now you can get a master’s degree in Computer Science from a prestigious university online. The New York Times has reported that the Georgia Institute of Technology is planning to offer the CS degree via the MOOC (massive open online course) model.
According to the Georgia Tech MS Computer Science program of study website, students can choose specializations in topics such as computational perception and robotics, which includes courses in artificial intelligence, machine learning, and autonomous multi-robot systems among student choices; interactive intelligence, which include courses in knowledge-based AI and natural language; or machine learning, which offers electives in the topic for theory, trading and finance, among other options.
As you surely know by now, it’s GeekWeek on YouTube. But in case you haven’t been keeping up with every theme, today is Brainiac Tuesday, its focus on science, education and knowledge – a particularly relevant topic for readers of this blog, we think.
We didn’t see any particularly semantic videos pointed out in the Tuesday Highlights. The recommendation of Wired and YouTube’s “How to Make a Giant Robot Mech” fed some hopes, but looks like the big guy owes his smarts to a human pilot rather than artificial intelligence.
That’s not to say there isn’t good stuff among the pickings. Steve Spangler’s Favorite Experiments is a kick, for instance. And who knew that a volcano caused the French Revolution? But we’d like to hear it for semantic web, tech and related videos, too, on this Brainiac day.
To that end, here are a few of our own recommendations:
- TED Talks: Tim Berners-Lee: The Next Web of Open, Linked Data. OK, it’s a gimme that at least one TBL video is going to be on this list (two if you count the Goat Edition of this particular talk). But if you want to get a good grounding in the semantic web, you’ve got to start at a key source.
Reasoning is the task of deriving implicit facts from a set of given explicit facts. These facts can be expressed in OWL 2 ontologies and stored RDF triplestores. For example, the following fact: “a Student is a Person,” can be expressed in an ontology, while the fact: “Bob is a Student,” can be stored in a triplestore. A reasoner is a software application that is able to reason. For example, a reasoner is able to infer the following implicit fact: “Bob is a Person.”
Reasoning tasks considered in OWL 2 are: ontology consistency, class satisfiability, classification, instance checking, and conjunctive query answering.
This year’s SemTechBiz Conference in San Francisco is already drawing experts using and developing semantic technologies in business, health care, the financial sector, public services, and beyond. But even if you’re completely new to Semantic Technologies, The Semantic Technology & Business Conference has sessions for you including an excellent tutorial by Lee Feigenbaum, co-creator of Semantic University.
Lee Feigenbaum is a leading expert in Semantic Web technologies and their applicability to enterprise IT challenges. As VP of Marketing & Technology at Cambridge Semantics, Lee helps ensure that the Anzo product suite continues to address customers’ ever-changing and diverse data challenges. Lee is an active member of the W3C Semantic Web standards community, currently serving as the Co-Chair of the W3C’s SPARQL Working Group, leading the design of SPARQL, the Semantic Web query language. Lee authored “The Semantic Web in Action,” a 2007 article in Scientific American. Read more
OWL, the Web Ontology Language has been standardized by W3C as a powerful language to represent knowledge (i.e. ontologies) on the Web. OWL has two functionalities. The first functionality is to express knowledge in an unambiguous way. This is accomplished by representing knowledge as set of concepts within a particular domain and the relationship between these concepts. If we only take into account this functionality, then the goal is very similar to that of UML or Entity-Relationship diagrams. The second functionality is to be able to draw conclusions from the knowledge that has been expressed. In other words, be able to infer implicit knowledge from the explicit knowledge. We call this reasoning and this is what distinguishes OWL from UML or other modeling languages.
OWL evolved from several proposals and became a standard in 2004. This was subsequently extended in 2008 by a second standard version, OWL 2. With OWL, you have the possibility of expressing all kinds of knowledge. The basic building blocks of an ontology are concepts (a.k.a classes) and the relationships between the classes (a.k.a properties). For example, if we were to create an ontology about a university, the classes would include Student, Professor, Courses while the properties would be isEnrolled, because a Student is enrolled in a Course, and isTaughtBy, because a Professor teaches a Course.
“If you don’t understand what your software engineers are talking about, perhaps it’s because they are using a vocabulary they invented for the problem they are solving.” This begins a white paper called, “The Business Value of Semantic Technology” by Chris Moran, CTO, Information Management Solutions Consultants, Inc.
Moran continues, “Engineers invent a vocabulary and data structure for each system they build and each problem they solve, and only the engineers who built the system understand this structure and vocabulary. Even other engineers must learn it in order to make the data usable. In most enterprises today, we have as many different ways to ask questions of our data as we have systems to store it. We have as many different vocabularies and data structures as we have systems. The problem is actually worse than it sounds….”
Triplestores are Database Management Systems (DBMS) for data modeled using RDF. Unlike Relational Database Management Systems (RDBMS), which store data in relations (or tables) and are queried using SQL, triplestores store RDF triples and are queried using SPARQL.
A key feature of many triplestores is the ability to do inference. It is important to note that a DBMS typically offers the capacity to deal with concurrency, security, logging, recovery, and updates, in addition to loading and storing data. Not all Triplestores offer all these capabilities (yet).
Triplestores can be broadly classified in three types categories: Native triplestores, RDBMS-backed triplestores and NoSQL triplestores. Read more
NEXT PAGE >>