I think part of the problem is defining what the "Semantic Web" or "Semantic Publishing" is. For me, it is being able to navigate information based on semantic content. For example, applied to web search, I'd expect the search engine to be able to present me with the topics present in my search results and allow me to re-rank/refine those results based on the presence of topics. If I search for cancer, I would expect the search engine to identify the topics within my search results (lets say: diagnosis, treatment, survival, research etc.) and let me rank results based on the relevance to topics. If I was interested in research, for example, then I'd indicate that via the interface and the results would update. This would seem obvious when it's a search for a term that you already know something about, like cancer, but it becomes powerful when your searching for something you don't know anything about. It allows you to learn and navigate much quicker. It's also easy to see its value when you extract this out to being able to navigate your social media and news stories.
There are startups today that have the technology to do this. Some rely on machine learning (including deep learning) to pre-build models that are then used to classify text based. Others build topic/concept models "realtime" based on the content you pass it. And others still rely on a linguistics approach (admittedly, not many recent companies take this approach for various reasons). One even has a tool that will let you search Google in a somewhat similar fashion to what I described. However, most are still looking for funding and some have been pulled away to focus on customer feedback/BI rather then the semantic web. Also, it can be difficult to start such a company given the amount of resources it needs to get going. Very difficult to bootstrap.
Whats the major difference? Engineering students have to do math, engineering principles and diverse team project subjects.
Engineering students must complete 2 team project subjects (aptly named Team Project 1 and Team Project 2) which each last a semester as well as a Thesis which lasts 2 semesters. The first team project I did was to construct a PC based oscilloscope (hardware in a wood book with a coax input for voltage and an rs-232 connection to a PC) and the second was an reminder keyring (programmable using flashes of light on a PC screen. Also needed to provide the software to set alarms at certain times). Both projects were completed between 4 students comprising of 2 software engineers, an electrical engineer and a computer systems engineer.
I have chosen to and am currently completing my thesis through the CEED project. CEED list projects on behalf of companies to be completed by students. Once a project is completed and documented appropriately the student can then submit the work as their thesis project. The company pays CEED for this service and retains all IP rights to the work. CEED then passes on a % of this money to student for their work.
This model seems to work well enough. My only criticism is the allocation of students to groups in team project. It isn't a rare occurrence that you get teamed with other students that either struggle to talk English or don't have a solid understanding of their discipline, thus making the completion of the projects rather difficult. But, only 50% of the subjects marks are on the final product while the other 50% is on documentation and planning (see the related link). I think this is a good enough compensation for group selection.
Coincidently, students of both IT and Software Engineering are required to complete 3 subjects related to the design and development strategies of software. See the course guided here (PDF).
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I think part of the problem is defining what the "Semantic Web" or "Semantic Publishing" is. For me, it is being able to navigate information based on semantic content. For example, applied to web search, I'd expect the search engine to be able to present me with the topics present in my search results and allow me to re-rank/refine those results based on the presence of topics. If I search for cancer, I would expect the search engine to identify the topics within my search results (lets say: diagnosis, treatment, survival, research etc.) and let me rank results based on the relevance to topics. If I was interested in research, for example, then I'd indicate that via the interface and the results would update. This would seem obvious when it's a search for a term that you already know something about, like cancer, but it becomes powerful when your searching for something you don't know anything about. It allows you to learn and navigate much quicker. It's also easy to see its value when you extract this out to being able to navigate your social media and news stories.
There are startups today that have the technology to do this. Some rely on machine learning (including deep learning) to pre-build models that are then used to classify text based. Others build topic/concept models "realtime" based on the content you pass it. And others still rely on a linguistics approach (admittedly, not many recent companies take this approach for various reasons). One even has a tool that will let you search Google in a somewhat similar fashion to what I described. However, most are still looking for funding and some have been pulled away to focus on customer feedback/BI rather then the semantic web. Also, it can be difficult to start such a company given the amount of resources it needs to get going. Very difficult to bootstrap.
"Could you live in a world without cheap and plentiful broadband internet access?" Unfortunately in Australia, we already do.
I'm a current student at the University of Queensland studying Engineering with a major in software. This degree is 4 years in duration.
UQ also offers an Information Technology degree which is 3 years in duration.
Whats the major difference? Engineering students have to do math, engineering principles and diverse team project subjects.
Engineering students must complete 2 team project subjects (aptly named Team Project 1 and Team Project 2) which each last a semester as well as a Thesis which lasts 2 semesters. The first team project I did was to construct a PC based oscilloscope (hardware in a wood book with a coax input for voltage and an rs-232 connection to a PC) and the second was an reminder keyring (programmable using flashes of light on a PC screen. Also needed to provide the software to set alarms at certain times). Both projects were completed between 4 students comprising of 2 software engineers, an electrical engineer and a computer systems engineer.
I have chosen to and am currently completing my thesis through the CEED project. CEED list projects on behalf of companies to be completed by students. Once a project is completed and documented appropriately the student can then submit the work as their thesis project. The company pays CEED for this service and retains all IP rights to the work. CEED then passes on a % of this money to student for their work.
This model seems to work well enough. My only criticism is the allocation of students to groups in team project. It isn't a rare occurrence that you get teamed with other students that either struggle to talk English or don't have a solid understanding of their discipline, thus making the completion of the projects rather difficult. But, only 50% of the subjects marks are on the final product while the other 50% is on documentation and planning (see the related link). I think this is a good enough compensation for group selection.
Coincidently, students of both IT and Software Engineering are required to complete 3 subjects related to the design and development strategies of software. See the course guided here (PDF).