Live Semantic Service Inform.com Takes $15m Investment
Semantic analysis service Inform.com announced today that the company has received a $15 million investment from Spark Capital. Inform analyzes content from online publishers and inserts links from a publisher's own content archives, affiliated sites or the web at large to augment content being published. The company says it already has more than 100 clients, including CNN.com, WashingtonPost.com and the Economist. Those who would contend that semantic web technology has not arrived can stick that in their pipes and smoke it.
Inform says its technology determines the semantic meaning of key words in millions of news stories around the web every day in order to recommend related content. The theory is that by automating the process of relevant link discovery and inclusion, Inform can easily add substantial value to a publisher's content. Inform also builds out automatic topic pages, something you can see around WashingtonPost and CNN.com. It sounds like a solid value proposition to me. This is the kind of thing that semantic technology is best at providing: making content machine readable allows the human mind to focus on genuinely creative work instead of determining things like what constitutes related content.
Standards?
No. Inform crunches straight text and outputs HTML. I asked whether they publish content with any standards based semantic markup and they said that actual publishing is up to publishers. That's a shame, I don't see any reason why Inform wouldn't participate in the larger semantic web to make its publishers' content more discoverable. Perhaps when you've got 100 live clients and now $15m in the bank, it feels like there's no reason to open up and play nice with a movement of dreamers having trouble getting other apps out of academia.
Different Approaches
While many publishers have been criticized for linking only to their own internal pages for reference (including many leading blogs) it's good to see that Inform at least provides the option of including outside links. That is, after all, one of the most important characteristics of the web - links from one site to another.
Inform indexes blogs, audio and video as well at standard web pages. It's a smart idea and similar to a number of related companies you may be more familiar with. Our own Alex Iskold runs AdaptiveBlue, a semantic company that offers related links tied to links already added by publishers and a semantic browser plug-in. SystemOne is an elegant system that offers related content automatically during the writing process. Lijit is a custom search engine of sorts, allowing you and your readers to manually search through a confined set of content.
The key way the above services probably differ is the degree of automation that they offer. Inform is highly automated, once a publisher sets up general rules for brining in related content. A publisher might say, for example, to insert a link to their own content on any terms they have more than 20 articles about from the past week, or that their affiliate network can provide content for with a certain minimum percentage of relevance.
There's some heavy math and linguistics going on at Inform and it's a good example of how proprietary technology is headed for the bank while open standards based approaches dawdle. In theory openness and standards should be clear winners in terms of ultimate value delivered to any company, someday. In the meantime, publishers can deploy Inform's semantic technology now.
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So many...questions:
1. Isn't this the OPPOSITE of semantic web, since they're sucking in unstructured data?
2. How does their relatedness stuff compare to Sphere?
3. How do their topic pages compare to Topix?
4. Where does this fit into their history? http://www.techcrunch.com/tag/inform.com/
5. Do beards make you a better or worse blogger?
6. Do you really think this semantic web crap will make people like RWW more? (If so, I need to figure out how Techmeme is Web 4.0...)
What a waste of domain name for a B2B service.
Marshall Kirkpatrick , you're correct that Semantic web has arrived, whether its adoption has been accelerated recently or not, it is going to be widely adopted in the future (no doubt about it).
I don't like hyping up of Semantic Web to be dubbed Web 3.0, as frequently been quoted here because Semantic Web is not new at all, it has been around since the mid-1990s. Sure there are techniques that have been applied recently to Semantic Web over recent years, but the discipline has been there all along before web-xxx was even born.
Here is one reason, why I think that calling it web 3.0 (or any web-xxx) it is going to give a false hope to developers out there because it makes them think that they can develop these capabilities easily. I would say that the technology is hard to implement, I mean your average developers who have been developing web 2.0 (or so) compliant apps. They would be disappointed to find out that Semantic Web development (concepts & algorithms) are not the typical common techniques that they adopted in developing their web 2.0, since it involves a whole lot of new difficult concepts that even with season developers (ie, many many years of experience) the concepts are no easy task to learn .
One discipline that is going to be vital in the development of Semantic Web is Machine Learning (ML). ML is vital in Semantic Web technology because it enables it to achieve an automated learning capability (just the same process as how human learns). ML is quite hard to learn, because of its requirement to understand high level mathematics which is not a cup of tea of every software developer. ML is not like you learn .NET or Java if you're new to them but you've been a developer in other languages. It is not something that you can grab a book and read it thru a weekend and on Monday, you're on your way to developing your first app. Some (or most) would find these topics something unlearnable, ie, there is no way they could understand them at all (regardless of how many years they've been involve in the software industry). The only solution to avoid learning them is to use some open source out there, which there are available. I myself had used Semantic related tools in the past for some projects that I got involved in, such as Protege Ontology Editor open source Java tool from Stanford.
There have been some seminars over recent years (or upcoming ones) in the application of machine learning in Semantic Web:
Machine Learning for the Semantic Web
Ontologies and Text Mining for Life Sciences: Current Status and Future Perspectives
In looks to me that Inform.com is using Machine Learning, since their system analyzes content, and this is a machine learning task. It learns about the contents of documents and decides what classification that a particular document belong to.
This is why I think that as the catch-phrase web-xxx evolves to higher version (web 3.0 or web 4.0 and so forth), developers will disappointed since the technologies to learn in order to develop higher version web-xxx application are simply unlearnable, so they won't be in there to develop such technologies. The implementation of Semantic Web would simply be restricted to companies and developers who can implement them.
PS: For readers here at RWW who are interested in Semantic Web for reasons such as learning , discussion, FAQ, then I would suggest that you subscribe to the Semantic Web discussion forum at:
semanticweb@yahoogroups.com
I have been on this list for many years. I don't do any Semantic Web development at the moment , however I like the forum, since many new things (research topics, new ones) are being discussed over there. My interest in this list, is basically just to watch out for interesting topics and applications.
Retroactive advert embedding. Mmm sounds like a plan. They should do well given the number of large unwieldy sites out there looking to monetise.
LinkedWords.com anyone? They‘ve been around for year and a half, as far as I know, known to be dealing with in-text contextual links and tackling the basic semantic (semantic URLs, paths, etc.) aspects of the Web. From what I see none of the sites mentioned above goes anywhere close to what LinkedWords seems to be reaching in terms of traffic today relying on their basic, but as it seems effective, semantic contextual approach.
Cheers
my 2 cents