Still No. 1 Blog For “Google Monopoly”

As of the writing of this post, Evolving Trends is still the No.1 blog for the keywords “Google Monopoly” on Google itself, now for over 3 years, unchallenged.

Google results link for “google monopoly” (and probably for any search phrase containing google and monopoly)

http://www.google.com/search?hl=en&source=hp&q=google+monopoly&aq=0&oq=google+mono&aqi=g9

This is with no search engine optimization, whatsoever.

May be the reason there is no serious challengers in this niche topic is that very few people ever think of whether Google is a monopoly or not, so it’s not a traffic generator, and, therefore, of little to no interest to the news media.

Hmm.

Update: September 7, 2009

Within just a week or so of posting this note, Google announced that they’re launching an online version of Monopoly. They’ve also added Google News results referring to that, so now the #1 result for the keywords “Google” and “monopoly” is their own news… about the game!

The Evolving Trends article “Is Google a Monopoly” is #2 in Google’s search results, under the Google News results.

Update: September 9, 2009

Today was launch day for Google’s Monopoly City Streets, the online Google Maps version of the board game Monopoly.

The launch site, http://monopolycitystreets.com has been down all day.

In the meantime, a lot of people are clicking on the Evolving Trends “Is Google A Monopoly?“ article (on this blog), which I’m hoping will get people to question whether Google is a monopoly or at least a potential monopoly. No many people normally think about such issue, despite it being a very important one.

As you can see from the charts below, the article used to get about 10 hits a day even though it has been the #1 result on Google search for the words “google” and “monopoly” for over 3 years now (see search results link above)

Traffic to this blog after the Google Monopoly (game) announcement

Search terms hit count on WordPress (from Google and other search engines)

September 18 Update

The top three results, including the Evolving Trends article at #1, are now showing above the News links.

September 19 Update

Google has completely buried this article after I expanded on the footnote to talk about Google practicing vigilante justice with site owners. The article didn’t just drop to #2 or even the 2nd or 3rd page. It has been completely buried deep in search results. This is obviously an act of censorship. And there you have it, the power of Google.

Related

Is Google A Monopoly?

Wikipedia 3.0 (3.14 Years Later)

Author: Marc Fawzi

License: Attribution-NonCommercial-ShareAlike 3.0

I’ve just received a couple of questions from a contributor to an IT publication who is writing about the state of the semantic web.

I’m taking the liberty of posting one of the questions I received along with my response.

<<

[The Semantic Web has seen] several years of development. For instance, there’s been steps to create Syntax (e.g., OWL & OWL 2, etc.) and other standards. Various companies and organizations have cropped up to begin the epic work of getting ‘the sematic web’ underway.

How would you characterize where we are now with the web. Is Web 3.0 mostly hype?
>>

RDF, which emerged out of the work being done on the semantic web, is being used now to structure data for better presentation in the browser. It’s being used by Google and Yahoo. So you can say that the semantic web is starting to bear some fruits. But unlike OWL-DL, RDF does not have the structure to implement a logic model for the given domain of knowledge, which is required by machines to reason about the information published under that domain. However, RDF and RDFa (and other variations) are perfect for structuring the information itself (as opposed to the logic model for the given domain of knowledge) so the next step will be to use RDF to structure information for machine processing, not just for browser presentation, and that would be combined with the use of domain-specific inference engines (which, in this case, would combine logic programs and OWL description logic for the various knowledge domains) to supply a pan-domain AI, which is fundamental to any attempt to making machines ‘comprehend’ the information on the Web, per the semantic web vision.

The “hard” problem with the semantic web is not the natural language processing, since we don’t really need it right at the start: we can always structure the information in such a way that it can be processed by machines and then comprehended using the aforementioned pan-domain AI, or, in the case of search queries, we can come up with a query language with proper and consistent rules that is easy to use by the average educated person, such that the information/query is machine-process-able and may be comprehended using domain-specific AI.

The “hard” problem is how can all the random people putting out the information on the Web agree to the same ontology per domain and same information structuring format when they do not have the training or knowledge to even understand the ontology and the information structuring format?

So both ontology creation/selection and information structuring has to be automated to remove incompatibilities/variances and human errors. But that’s not an easy task as far as the computer science involved.

However, instead of hoping to turn the whole web into a massive pan-domain knowledgebase, which would require that we conquer the aforementioned automation problem, we can base our semantic web model on expert-constructed domain-specific knowledgebases, which by definition include domain specific AI, and which have been in existence for some time now, providing a lot of value in specific domains, like banking.

The suggestion I had put forward three years ago in Wikipedia 3.0, which remains to date as the most widely ready article on the semantic web with over 230,000, was to take Wikipedia and its set of core contributors, i.e. the experts, who are estimated at 30,000 people as of 2006, and use train those 30,000 experts to help build the ontologies for each domain and properly format the information already on Wikipedia so that a pan-domain knowledgebase engine can be built on top of Wikipedia, which would be able to reason about information in all domains of knowledge covered by Wikipedia, resulting in the ultimate answer machine.

The Wikipedia 3.0 article and some of links there describe how that can be done at a high level as well as some implementation ideas. There is nothing ”hard” there except the leadership problem.

It’s clear that the leadership problem has not been tackled, especially having seen how Jimmy Wales started and quit a VC funded search startup after I had penned the article. Taking VC money (and being part of that model) is not the right way to do it. The right way is to carry it out with community support as a non-profit venture, not supported by VC funds. That’s partly because any such startup would require the free labor of those 30,000 Wikipedia contributors in their roles as domain-specific experts. So it just wasn’t going to work out to expect those contributors to work for free so that Jimmy Wales can make a fortune.

Toward A More Stable State

You have to get to the philosophical principles of the capitalist system.

It is designed as a competitive game of hunting for a scarce resource (money) and everything else, like me and you talking about this, is just art or noise.

There is nothing more to capitalism than squeezing the most juice we can get out of everything, including other people… and time!

In order to do that, we must get more and more efficient all the time, in a never ending trend. Yet every evolving system strives for an equilibrium and ultimately comes to an end.

I believe what we’re seeing right now with capitalism is the up and down movement (or oscillation) toward an equilibrium.