It is almost impossible to drive SEO today without semantically valuable, intelligent and industry-targeted language content.
After decades of fine tuning, search engines have become language- and industry expert “examiners” of web page content:
“Provide high-quality content on your pages, especially your homepage. This is the single most important thing to do.” (Google)
Connection is everything : “Make sure that other sites link to yours. (Google)
Before the FIFA world cup football game between Germany and Brazil in 2014 (which Germany won 7-1), Google returned 210 results for the unique word combination “Brazil Blitzkrieg”.
The morning after – 503 000.
NGramdata’s spiking “hot” web word combinations can be incorporated into client website content, -blog posts and online social marketing to drag enterprise brands higher up search returns through content linkages trending these word combinations.
You are the website developer for an accounting firm, auditor or corporate services provider…
Your client is anxious to be sharply visible online to local entrepreneurs that might be planning to set up a corporate structure for which they could offer a wide range of services.
NGramdata dynamically creates an insertable array of currently hot trending word combinations that act as textual “virtual markers” to easily add to client Twitter-, Facebook- and Reddit postings, as well as forums, blogs, media comments, business announcements, press releases, online classified ads or industry-specific technical articles.
Pinterest allows users to visually share- and discover new interests by posting images or videos.
“…there are more than 3 billion boards with 175 billion Pins…” (Hui Xu, Head of the Pinterest Discovery team)
Searching for images on Pinterest poses interesting semantic problems:
A search for “turkey filled with a duck stuffed with chicken for thanksgiving dinner” does not return a single result… but “turducken thanksgiving dinner” results in multiple returns.
Quality of matched search returns falls drastically with an increase in the number of keywords.
With NGramdata’s mix of word combinations, long input queries using different grammar structures with equivalent semantic meaning can be translated into powerful searches to yield result-optimised search returns:
“turkey filled with a duck stuffed with chicken”, is powerfully truncated to semantically equivalent “turducken”.
You have special energy related interests with team presence in Russia, Libya and Iraq.
You need to stay on top of security issues in these countries across many regions – in real time.
Using any of the large search engines to get information poses the problem:
What search words do you use?
Assuming that the region of Chechnya is relevant to you, do you search for..
“Security situation Chechnya“?
If an online report exists that covers “Security situation Grozny“, you could enter another search to cover the keyword “Grozny“…but if relevant online report titles do not include “Security situation“ but rather “Military- and other threats“, it starts to become very difficult to cover all search word permutations…
NGramdata scans the web 24/7 for NGram sets as base to provide a powerful selection of search keywords…
Fulham FC football players are staying at a local hotel preparing for their next match. The hotel owner overhears players heatedly debating difficulties with team tactics and strategy.
The owner approaches a betting shop with a large bet based on what he heard.
In the world of sports betting, new information can dramatically change betting lines and profit margins.
The betting shop realises that the hotel owner is sitting on information that could immediately alter the day’s betting profits. For the hotel owner this information is a unique betting asset that retains value as long as staying unrevealed.
To reset odds and minimise losses the betting shop needs to verify relevant new information in real time. It is difficult when online searches cover a large set of possible input keywords : “latest developments Fulham FC” might bring up many pages of largely irrelevant results if it is not known if relevant results could include “injuries”, “team selection”, “tactics”, “manager issues”, “pitch” or even “weather” phrases.
NGramdata’s real time word combinations powerfully help with this.
The betting shop only needs to do a quick check of NGramdata’s latest trending word combinations to narrow down news of short term issues around Fulham FC i.e. “last-minute changes to the Fulham line-up”.
PEPPER (manufactured by Softbank) is a unique personal humanoid that uses special sensors to detect people’s moods and how they behave:
“For the first time in human history, we’re giving a robot a heart, capable of learning and expressing emotions.” (Softbank CEO, Masayoshi Son)
Pepper is cutting edge technology – emotions are very hard to machine-capture… but even harder for AI is to semantically process all the possible natural language permutations of the input instructions it might receive.
There are many ways of asking : “make me a cup of tea” using different word combinations : “brew me a cup of tea”… “I need a cuppa”… ”stew me some Red Bush”… “a mug of tea will do”…
Making sense of all individual words in conversational phrases to create a desired response is difficult. Both smaller keyword sets as well as larger conversation chunks need to be amalgamated to meaningfully react to given instructions.
NGramdata parses small groups of words into larger and larger word sets to create natural language semantic paths that enable AI to more effectively parse keyword sets into the full semantic whole.