Parse.ly Brings A Dash of Semantics To Online Publishers. Online publishers and other content providers have a new analytics tool to help them understand what their readers care about and use that information to better connect them to their sites’ relevant and compelling content. Launching today is Dash, based on the predictive content analytics platform Parse.ly. The technology crawls every article page for Parse.ly’s publisher-partners, and analyzes, in real time and at scale, the text to identify relevant topics to group related content together. Behind this lies natural language processing technology, which uses language queues hidden inside the text to determine its affiliated topics.
To date Dash has extracted over 350,000 unique topics through all the URLs is has crawled during private beta for a healthy taxonomy of topics across the web being consumed by users. “Most analytics tools, like Google Analytics and Omniture, are one-size-fits-all,” says Sachin Kamdar, co-founder and CEO. Bing vs Google, the quiet semantic war « Shepherd’s Pi. On Wednesday night I had dinner at a burger joint with four old friends; two work in the intelligence community today on top-secret programs, and two others are technologists in the private sector who have done IC work for years. The five of us share a particular interest besides good burgers: semantic technology.
Oh, we talked about mobile phones (iPhones were whipped out as was my Windows Phone, and apps debated) and cloud storage (they were stunned that Microsoft gives 25 gigabytes of free cloud storage with free Skydrive accounts , compared to the puny 2 gig they’d been using on DropBox ). But we kept returning to semantic web discussions, semantic approaches, semantic software. One of these guys goes back to the DAML days of DARPA fame, the guys on the government side are using semantic software operationally, and we all are firm believers in Our Glorious Semantic Future .
Here’s a quick and friendly video by Metaweb explaining their approach to representing semantic meaning: Top 10 Reasons Not To Do a Semantic Web Project. The Semantic Web is a great vision that began almost ten years ago, but at this point, the failures outnumber the successes by a very large margin. In order to move closer to realizing the great theoretical vision of its founder, Tim Berners-Lee, it is important to understand the common pitfalls of semantic undertakings.
Let's examine the top 10 failures of Semantic Web projects and companies one by one. 1) Semantic Web is Anti-Era [login] The Semantic Web world is like a lumbering, giant group of smart people stumbling around without too much direction or vision, while all around them, smart and savvy entrepreneurs are creating amazing social, content, and mobile projects and companies at a comparatively break-neck speed and a fraction of the cost. 2) Too Much Work Before Getting In-Front of Clients 3) No Talent To Hire or Partner With Remember how difficult it was to get a good Java programmer to work for your company or project in the late 1990s?
Thoughts on software, innovation and people - Journal - The puzzle of semantic web adoption. I am a believer in the rise of the web of data. In fact I am CTO of Talis which is investing heavily in semantic web technologies. So don’t take this the wrong way but I can’t help but feel the semantic web community is ignoring a vital part of the semantic web jigsaw and this is creating a major credibility problem between it and large parts of the technology community. I am concerned because I think that the semantic web currently lacks two critical things that drove mass adoption of the web. To be fair the W3C has created a semantic web outreach group and Talis has two representatives on this, so we are doing our bit to help to spread the word :-) but this is only going to really work if the semantic web community really understands what the major missing pieces are for mass adoption.
Today, looking at the conversations in the semantic web community, I don’t think the real barrier is being seen clearly. So here is my personal view on what is going on here. The web had 0 day utility. Top-Down: A New Approach to the Semantic Web. Earlier this week we wrote about the classic approach to the semantic web and the difficulties with that approach. While the original vision of the layer on top of the current web, which annotates information in a way that is "understandable" by computers, is compelling; there are technical, scientific and business issues that have been difficult to address. One of the technical difficulties that we outlined was the bottom-up nature of the classic semantic web approach.
Specifically, each web site needs to annotate information in RDF, OWL, etc. in order for computers to be able to "understand" it. As things stand today, there is little reason for web site owners to do that. The tools that would leverage the annotated information do not exist and there has not been any clearly articulated business and consumer value.
Which means that there is no incentive for the sites to invest money into being compatible with the semantic web of the future. But there are alternative approaches. Semantic Web: What Is The Killer App? The Semantic Web has been in the making for some time and people think it is nearing maturity. We have written about this trend extensively, with our two most notable posts being an analysis of the challenges of the classic bottom-up approach and the promise of the new top-down one. Regardless of how the Semantic Web will come about, for it to flourish it needs to hit the mainstream. There is no way that consumers will appreciate the elegance and mathematical soundness of RDF and OWL. People don't care about math, they care about utility and even more, about fun. Whatever it is, it needs to layer an understanding of semantics on top of a consumer application.
In this post, we analyze several existing and potential applications of semantic technologies and look for the killer app. Natual Language Understanding Since the beginning, the Semantic Web has been associated with Artificial Intelligence. Natural language processing has been the Holy Grail of AI for awhile now. Genie In The Bottle.