Commercial Intelligence

I started to use Radar Networks’ Twine at the invitation of CEO Nova Spivak after writing this earlier this year (also see this). I enjoyed it for a while, especially because a lot of technology folks were hooking up with each other, especially the semantic web community, on Twine. But I found it  tedious to work through beta issues and to be bothered with recommendations or news about who was saying or bookmarking things about what. (I should have turned off the emails sooner!)

I was disappointed that Twine was taking an apparently folksonomic approach to tagging. It was as if Radar Networks was riding semantic web buzz without really embracing it openly or sharing the momentum that the invite-only community was investing in.  That may not sound fair - I believe that there are semantics in the back room, but that’s how it felt and it’s still the way it looks.  But probably the worst part is the process that you have to go through to add a bookmark - which is the whole point, of course!  (I ultimately sacrificed popup blockers, but the process still seems laborious compared to other alternatives.)

I stumbled across Zigtag almost accidentally while working for a VC firm with a portfolio of semantic startups. What I like most about Zigtag is that they make it obvious that they are building an ontology of tags and encourage users to select semantic tags (i.e., concepts) rather than folksonomic “words”.  They also provide tools for managing tags that allow you to move smoothly and incrementally from a folksonomic to a more semantic approach.

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I have previously written about the lack of a common upper ontology in the semantic web and commercial software markets (e.g., business rules).  For example, the lack of understanding of time limits the intelligence and ease of use of software in business process management (BPM) and complex event processing (CEP).  The lack of understanding of money limits the intelligence and utility of business rules management systems (BRMS) in financial services and the capital markets.   And, more fundamentally, understanding time and money (among other things, such as location, which includes distance) requires a core understanding of amounts.  

• Understanding events and processes takes time
• Oracle should teach Siebel CRM about location and money
• Ontology of time in progress - amounts needed  

The core principle here is that software needs to have a common core of understanding that makes sense to most people and across almost every application.  These are the concepts of Pareto’s 80/20 Principle.  A concept like building could easily be out, but concepts like money and time (and whatever it takes to really understand money and time) are in.  Location, including distance, is in.  Luminousity could be out, but probably not if color is in.  Charge and current could be out, but not if electricity or magnetism is in.  The cutoff is less scientific than practical, but what is in has to be deeply consistent and completely rational (i.e., logically rigorous).^[2] (more…)

As I discussed in Over $100m in 12 months backs natural language for the semantic web, Radar Networks’ Twine is one of the more interesting semantic web startups.  Their founder, Nova Spivak, is funded by Vulcan and others to provide “interest-driven [social] networking”.  I’ve been participating in the beta program at modest bandwidth for a while.  Generally, Nova’s statements about where they are and where they are going are fully supported by what I have experienced.  There are obvious weaknesses that they are improving.  Overall, the strategy of gradually bootstrapping functionality and content by controlling the ramp up in users from a clearly alpha stage implementation to what is still not quite beta (in my view) seems perfect. 

Recently, Nova recorded a few minute video in which he makes three short-term predictions: (more…)

Radar Networks is accelerating down the path towards the world’s largest body of knowledge about what people care about using Twine to organize their bookmarks.  Unlike social bookmarking sites, Twine uses natural language processing technology to read and categorize people’s bookmarks in a substantial ontology.  Using this ontology, Twine not only organizes their bookmarks intelligently but also facilitates social networking and collaborative filtering that result in more relevant suggestions of others’ bookmarks than other social bookmarking sites can provide.

Twine should rapidly eclipse social bookmarking sites, like Digg and Redditt.  This is no small feat!

The underlying capabilities of Twine present Radar Networks with many other opportunities, too.  Twine could spider out from bookmarks and become a general competitor to Google, as Powerset hopes to become.  Twine could become the semantic web’s Wikipedia, to which Metaweb’s Freebase aspires.

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