Tag Archives: semantic

Brazil Blitzkrieg: How FIFA, Search Engines & Semantics Mean Profit…

After more than a decade of fine tuning, Google (and other search engines) have improved ranking algorithms at magnitude. Gone are the days of multiple keyword padding – even with accurate grammar inside high-level semantic structures. Search engines have effectively become language- and industry savvy “examiners” of web page content – analogous to a university professor grading pages of a thesis.

It is essential to know what the search engine “professor” values:

“Provide high-quality content on your pages, especially your homepage. This is the
single most important thing to do.” (Google)

It is almost impossible to drive search engine optimisation today without
adding semantically valuable, highly intelligent, industry-targeted language content:

“Make sure that other sites link to yours.” (Google)

Before the FIFA world cup football game between Germany and Brazil (which Germany won 7-1), Google returned 210 results for the unique word combination “Brazil Blitzkrieg”. The morning after – 503 000.

When suddenly spiking “hot” web word combinations are incorporated at innovative semantic level into enterprise blog posts and other online social marketing, the enterprise brand can – with little effort and cost – be dragged into top-end search returns through content linkages to sites also temporarily trending these concepts.

Gatfol’s role is not merely to take web word combinations and find immediate synonyms, but to extract the meaning and related context of these and use it as an expansion linking factor. “Brazil Blitzkrieg” can powerfully become “German rumble in the jungle”, with strong search engine first page traction to a wider set of external sites. This broad concept catchment drives online exposure with more degrees of freedom around narrow keywords.

Gatfol drives hot web word combinations to sweep surrounding
enterprise concepts in real time for finely targeted semantic attacks…

Gatfol Salt…and Pepper


“People describe others as being robots because they have no emotions, no heart. For the first time in human history, we’re giving a robot a heart, capable of learning and expressing emotions.”
(Softbank CEO, Masayoshi Son)

Softbank’s PEPPER is a unique personal humanoid that…

…stands 4 feet tall with arms that move, but without legs, weighing 60 pounds…
…has a high-pitched boyish voice and speaks about 20 different languages…
…uses special sensors to detect people’s moods and how they behave…
…uses facial recognition technology to read and interpret emotions…
…interacts with the cloud to develop its own emotional capabilities…
…communicates using a tablet-like display mounted on its chest…
…provides friendly companionship for the lonely, sick or elderly…

Pepper is cutting edge technology – emotions are very hard to machine-capture…but there is something that Pepper technology will not be able to do…Pepper will not be able to semantically handle all the possible natural language permutations of the input instructions given to it by humans.

There are many ways of asking :  “make me a cup of tea” using different word combinations whilst ensuring overall consistent meaning with grammatically correct word combinations : “brew me a cup of  tea”… “I need a cuppa char” …”stew me some Red Bush”  …“make me a mug of tea”…

Current robot language processing abilities are linked to arbitrary parsing of elementary keywords from full human language grammar and syntax. Making sense of all individual words in conversational phrases to create a desired response is difficult. Robotic brains need to understand the meaning and context of sentences in relation to larger conversation chunks to be able to meaningfully react to given instructions. Gatfol parses small groups of words in larger word sets to create natural language semantic paths that enable robots to more effectively integrate keyword sets into the full semantic whole.

 Gatfol is the Salt to Pepper and all its future friends….

Could Permutations Kill Ray Kurzweil’s Google AI Dream?

I remember reading somewhere online about a recent interview
(amongst many) with Ray Kurzweil – Director of Engineering at Google and the
world’s staunchest AI visionary – where he mentioned something about his plans in the line of…

to get the Google computers to understand natural language, not just do search and
answer questions
based on links and words, but actually understand the semantic content

I cannot remember where I saw the interview and will Google it…

…but I have a problem…

I know my keywords are fuzzy and I use Gatfol to show me some sample permutations on my input
…and realise that with 13 keywords and only 10 same-meaning single word replacements for each
keyword, as well as allowing for multiword to semantic equivalent multiword replacements, I am
looking at a minimum of 10 000 000 000 000 000 000 000 000 different ways to phrase my input
…all with the same basic meaning…

I also realise that I want a very specific search return that semantically equates to my input and
does not just hit and return pages corresponding to the trillions of different keyword combinations…

I quickly calculate that just by browsing my own thoughts and looking at my immediate
physical surroundings, I can theoretically come up with trillions upon trillions of meaningful
search input queries…each with trillions upon trillions of semantically equivalent phrasings…

The thought hits me that if Google wants to morph into Ray Kurzweil’s vision of a search
mind with which I can communicate and interrogate as if human – perhaps in five to ten years –
it will have to perform massively scalable multiword to multiword semantic equivalence transformations…

…Gatfol currently does this…

Gatfol – The Second Child of Internet Search Technology…

Can you see the future ?

Amit Singhal: Senior Vice President (Google Search)
…SXSW conference 2013, Austin Texas USA…

“having humanity’s knowledge on the Web is not enough, you have to understand it”

“search challenges: knowledge graph, speech recognition and natural language”

 “…the perfect search engine should know
exactly what you need and give you what you want”

“our dream is to become the star trek computer”

“people have completely come to expect search engines to work and
the questions they have been asking have gotten harder and harder”

“…voice is clearly crucial for the future”

“…a star trek computer you could type or talk
and connect to would be crucial to (search) success”

 …all good and well, but why can we easily generate millions
of semantically equivalent search query replacements while
Google still cannot return semantically equivalent result groupings?

If I ask meaning wise the same thing – just with different
keywords – shouldn’t my return results be semantically consistent?

Why do we have to laboriously guess, replace and aggregate
synonyms as keyword alternatives to improve returned search results?

“….I would really like to find the web meeting discussion where the
participants discussed help and assistance to Indian farmers to help ease
yield decreases and with immediate concern funds and finance issues on the ground….”


 “…I would actually like to get the web talk where the
participants discussed support to Indian farmers to improve yield
net gains and with the critical thought of money issues on the ground…”


 Not one of the top ten Google return
results for my first query is replicated in even
the top
hundred results of my semantically equivalent second search query…


We dream of talking to Star Trek computers but are still
stuck for optimisation in the “caveman-speak”
three word input search query universe…


Beam me up Gatfol…

The Solution: Gatfol Web Text Simplification and Search Augmentation…


Gatfol is a provisionally patented, natural language, browser-based mobile technology that opens up the web to challenged readers in Africa and emerging economies worldwide. Gatfol technology simplifies web text instantly to match the preferred reading level of any language challenged (semi-literate) web user.

The Gatfol technology traversed a 9-year development period before patent application. This solid ground level base enables Gatfol to efficiently “translate” even large volumes of web text very quickly into simple reading components. The technology is unique in that it provides for a fast multiword-to-multiword stepwise crystallization of natural language (English) from semantic complexity to semantic simplicity and vice versa.

Gatfol also instantly translates search engine queries (i.e. Google) typed in simple language by reading-challenged users, into sophisticated web language to enable real-world keyword matching – even for complicated topics in technically advanced industries :

Gatfol has operational code frameworks available to run as a Cloud-based service or in case of confidential data streams – as a local master and slave technology to quickly simplify web language – even in-line and in real time. This confidential data stream technology can run on as simple a platform as a single desktop machine or ordinary Windows network set-up.

As an adult further education language tool, Gatfol is very cost effective. Most of the large African literacy programs carry a cost per semi-literate learner per year of around $50. Gatfol web text simplification technology brings down the costs per semi-literate learner substantially. Gatfol calculates that for just $1.80 per year, the English vocabulary of a challenged reader can be increased a HUNDRED fold – from a vocabulary of 200 words to a vocabulary of 20 000 words.

Web-enabled mobile devices using Gatfol technology also give disadvantaged users an opportunity to “see” online web language of a higher semantic complexity than by using the relatively basic English language material covered by further education programs.

Gatfol is words…words are power…