This software application relates to information processing and more specifically relates to scalable stepwise analysis of unstructured natural language data to generate multiple alternative semantic views of text and assist with simplifying and speeding-up of real-time analysis of large volume inline data streams. Enabling computers to understand language remains one of the hardest problems in text analysis. One simple increase in extraction complexity is to perform keyword synonym replacements – usually on a single-to-single word basis. Even words which are normally interchangeable can lead to totally different meanings when used in different contexts.
It is very difficult for machines to semantically interpret longer search queries so as to deliver meaningful extraction results.
Existing semantic analysis engines are weak at converting complex contextual meanings in search inputs to meaningful results. Currently, certain defined domains – for example, airline booking systems – operate in this way.
The goal of all-encompassing semantic search has not yet fully been realized, despite ongoing efforts to index, categorize and associate concepts in multitudes of datasets worldwide.
It would be advantageous to have a completely autonomous self-replicating system that is able to build a contextual language model so that search strings can be interpreted more accurately by language analytic engines, without the need to categorize or index existing content.
It is the object of the Gatfol software application to provide a massively scalable but easy-to-install system in the form of a simulated Hadoop method (distributed multiple redundant master and slave nodes) for the stepwise crystallization of natural language (English) text input from semantic complexity to semantic simplicity on single ordinary desktop computers to enable extremely fast searches of multi-keyword groups to be made into very large data streams.
The crux of the application is the comparison of left-right ambidextrous grammar signatures for all keywords in the search input and the application of Markov chain analysis to create multiword groups of similar semantics and intact grammar corresponding to the original input. The application comprises multiple redundant local machine based master and slave software nodes to process input in parallel to ensure extremely high throughput speeds at very large input volumes, regardless of machine- and CPU hardware configurations . The application engine as well as all data inflow into the application and all resultant outflow is fully contained on the local machine. At the next stage, the non-popular words are linked in two-word groups from left to right with the last word of any preceding two-word group forming the first word of the next two-word group.
Each two word group is then analyzed according to its ambidextrous grammar signature as follows: the reverse signature of the first word and the forward signature of the second word are obtained. Next the node signature database is searched to look for close signature matches for the ambidextrous “word group” signature. The previous stage is repeated for each of the other two-word groups in the search string, which in this example is the second two word group, “spring water”. Using the substitute word or words for “cool spring” and “spring water”, and repeating the procedure with the substitute two words (e.g.
By repeating the substitution procedure in all the previous stages a specified number of times, it is possible to obtain multiple alternate words for the extracted non-popular words. While the method described above enables the extracted non-popular portion of the search string to be substituted with semantically similar words, it does not necessarily follow that the semantically similar words will be grammatically correct when substituted back into the original search string. In this case, the meaning is also not as originally intended because of the multiple meanings of the word “spring”. To overcome the problem of grammatically incorrect alternative search strings, each node application applies additional steps by means of which grammatically incorrect alternative strings can be excluded.
For example, in the case of the alternative string, “Where can I get season water”, it is very unlikely that “get” will appear within the list of words that commonly precede “season” or that “water” will appear within the list of words that commonly follow “season”.
If the word “fresh” is identified as semantically similar to “cool spring”, the string, “Where can I get fresh water?” would be checked for grammatical correctness by seeing whether the word “get” commonly precedes “fresh” and whether “water” commonly follows “fresh”. Once multiple alternative strings of diminishing grammar- and semantic complexity (crystallization) have been generated, they are simultaneously or in very rapid succession input into search streams (i.e.
From the perspective of the user of search analytics all functioning described above is completely hidden and operates in stealth ‘in the background.
Note how the small “coffee cup” negative space adds “coffee” to the full “coffee shop” semantic image. When building the software node word relationship databases, Gatfol uses a proprietary technology called SIFT. Using SIFT, Gatfol is extremely effective as base technology in automatic machine tagging of images covering detailed image components. Above we could be looking at a work bench with a painters cap and several containers with dark- and yellow paints and some metallic tools, perhaps a green picnic blanket with tea or coffee and vegetation against a light sky, perhaps a green corn field with white grain silo’s etc. Each of the supplied tags above is iterated with SIFT through wider and wider semantic word groupings, continuously checking back to available tags for matchings (Gatfol semantic crystallization is not only many-to-single down but also single-to-many upwards as well as multiword to multiword).
Both Anders Breivik and Jared Lee Loughner posted substantial digital repositories before their extremist acts. As is mostly the case, online threats and circumstantial postings contain little or no clear-cut hits against “danger word” listings. The Gatfol semantic engine picked up these danger-words but flagged with extreme sensitivity the word combination “media interviews”. Unlike almost all competing technology available today, Gatfol provides robust parallel processing power from even simple desktops or laptops. With a simulated Hadoop multiple master-and-slave node architecture built around simple but robust WindowsTM executable files and with multiple fallback redundancies around both master and slave functions, as well as all nodes individually carrying full word relationship databases, reliability of throughput is ensured – especially critical in large volume streaming functionality. With a base in ordinary executable files, Gatfol also secures legacy hardware and OS (Windows XP and older) functionality and easy portability in instances of local machine OS upgrades.
Gatfol standalone architecture can be easily incorporated into wider distributed processing architecture including full Hadoop – with corresponding increases in throughput performance. To ensure absolute stealth and security on a local machine basis no online update calls are made by the application. 1) Terrorism: Includes media reports on the activities of terrorist organizations both in the United States as well as abroad.


5) Immigration: Includes reports on the apprehension of illegal immigrants and border control issues. 6) HAZMAT: Includes reports on the discharge of chemical, biological, and radiological hazardous materials as well as security and procedural incidents at nuclear facilities around the world, and potential threats toward nuclear facilities in the United States. 7) Nuclear: Reports on international nuclear developments, attempts to obtain nuclear materials by terrorist organizations, and stateside occurrences such as melt downs, the mismanagement of nuclear weapons, releases of radioactive materials, illegal transport of nuclear materials, obtaining of weapons by terrorist organizations, and breaches in nuclear security protocol. 8 ) Transportation Security: Reports on security breaches, airport procedures, and other transportation related issues, and any of the above issues that affect transportation. 9) Infrastructure: Reports on national infrastructure including key assets and technical structures. 12) Public Safety: Includes reports on public safety incidents, building lockdowns, bomb threats, mass shootings, and building evacuations.
13) Reports on DHS, Components, and other Federal Agencies: Includes both positive and negative reports on FEMA, CIS, CBP, ICE, etc.
This is a current list of terms that will be used by the NOC when monitoring social media sites to provide situational awareness and establish a common operating picture. This white paper and updates to it are made available for general information purposes only and is in no way binding upon Gatfol. The contents of this paper are protected by the patent laws of the United States and other jurisdictions. This entry was posted in Gatfol White Papers and tagged confidential data streams, Homeland Security, Military on February 20, 2012 by Gatfol Team.
High penetration, dual-view technology, explosives and narcotics detection alert and compliant with EU and US regulatory standards. Rapiscan’s radiation detection systems enhance security and counter smuggling of radioactive material.
Rapiscan’s broad range of radiation detection products meets the technical and operational requirements of our customers’ radiation inspection applications. The latter could be real-time security image- or video tagging”, inline analysis of Twitter, Facebook or security scanning of mobile phone SMS and voice data.
To return accurate results, search- or summarization algorithms must be able to apply some form of language interpretation to query strings. Thus the word “picture” may have the synonym “photo” so that analytical searches against “picture of Grand Canyon”, also extracts “photo of Grand Canyon”. If a search is for “history of motion pictures” then the word “pictures” must not be substituted with “photos” because the string “history of motion photos” is meaningless.
A search for “arm reduction” probably has to do with cosmetic surgery whereas “arms reduction” relates to reducing stockpiles of weaponry. As illustration, a search on Google TM for “Software companies founded before 1990 with a current turnover of more than $100 million” yields a list of largely irrelevant references, even though the search query is perfectly clear to a human and the information is doubtless available on the Internet. Thus the term “JFK” in an airline booking system means only John F Kennedy International airport in New York, not to the former US president or other terms that may have these three letters as their acronym. The main problem is the enormity of the task involved in performing such identification and association, which requires huge structured lexicons and ontologies as guides. Any amount of nodes can be used with processing speed increases proportional to the volume of nodes applied. No programmatic calls are made outside of the local machine for any reason at any time whatsoever. Popular words are identified as those words with a total frequency in each software processing node word relationship database that is higher than a predetermined threshold – in other words, those words that appear very commonly in the total body of text as indexed by the node. The words “where”, “can”, “I” and “get” will be identified as popular words, with the remaining words “cool spring water” being non-popular words. The forward and reverse group signatures are combined into a single left-right  “word-group” signature. By comparing the ambidextrous signature to the word signature database and looking for close matches, single words can be found that are semantically similar to the two word group, “cool spring”. In this way, one or more other words are identified that are semantically similar to “spring water”. The alternate words can be a string that has any number of words fewer than the extracted non-popular words.
For example, in the search string, “Where can I get cool spring water?”, if the word “season” is identified as semantically similar to the two words “cool spring”, substituting “season” into the original string yields the phrase, “Where can I get season water?” which clearly is not grammatically correct. In most cases, where the substituted words yield a sentence that is grammatically incorrect, the meaning of the alternative string is different from the intended meaning of the original string, but where the substituted words yield a sentence that is grammatically correct, the meaning is generally consistent with the original meaning.
To do this, the semantically substituted words are first substituted back into the original search string. In both cases, the answer will be in the affirmative and, at the final stage, the string “Where can I get fresh water?” will be identified as an alternative string for “Where can I get cool spring water?”. The user interacts with the search analytics application(s) in exactly the same way as before, but receives output results based on the multiple stealth alternatives.
We do not have “coffee” as a concept, neither do we have “shop”, and NEITHER DO WE HAVE “coffee” and “shop” as a combinational concept. As long as ambiguous image content descriptions are available, Gatfol semantic intelligence is powerful enough to stepwise crystallize the ambiguities and provide large-volume detailed tags that all relate semantically.
If the shiny object at the bottom of the image is not a small mirror, work tool, ball bearing, light bulb or lens – but a teaspoon, the white object with dark contents is likely a tea or coffee cup with contents and not a bowl with soup – if coffee, then the white object with partial covering of a “hat” at right is not the “best fit” semantically. In an ideal world we should be able to track ambiguous social network traffic to pinpoint individuals and groups narrowing in on behaviour that can be harmful to society.
Anders Breivik’s online manifesto contained the following sentence : “I simulate various future scenarios relating to resistance efforts, confrontations with police, future interrogation scenarios, future court appearances, future media interviews etc.


With all data streams staying local to the processing machine, field agents or operators do not require online access for operation in any way. Updates can be made freely and easily at any time on a manual basis from the Gatfol website by any user. Also included under this category are reports and responses to suspicious powder and chemical or biological agents.
Reports including threats toward and incidents involving rail, air, road, and water transit in the United States. Reports including articles related to threats against American citizens, political figures, military installations, embassies, consulates, as well as efforts taken by local, state, and federal agencies to secure the homeland. By reading this white paper you understand that there is no supplier-client or advisory relationship created between you and Gatfol. You may print a copy of any part of this blog for your own personal, noncommercial use, or for reasonable distribution to directly interested third parties, but you may not copy any part of the white paper for any other purposes, and you may not modify any part of the white paper. Through continual product development, Rapiscan Systems reserves the right to change specifications without notification, please contact your sales representative for more information.
Often the same words have different meanings in different contexts and small differences in sentence structure can lead to totally different meanings.
Most of the time interpretation will be limited to simple keyword determination for extraction against data repositories. As another example, if an analysis includes a search for “HP wide screen monitor” and we operationally substitute the synonym “detector” for “monitor”, and “shutter” for “screen”, completely irrelevant returns would be delivered.
When longer sentences are involved, erroneous permutations become exponentially more complex.
This characteristic is critical for application in security classified military- or other confidential data stream environments.
In this manner a crystallization from high grammar- and semantic complexity to simplicity is achieved.
Combining the results of both iterations yields a number of two word strings that are each semantically similar to “cool spring water”. In this example, the single word “juice” could, for example, be identified as semantically similar to “refreshing liquid”. For example, if 5 non-popular words were extracted, then alternate word string of 4, 3, 2, or 1 word(s) can be generated. Then each substituted word is analyzed within the original string to see whether the words preceding it and following it are words that are associated with the substituted word by a predefined degree. We have a unique negative space of  “coffeeshop” that’s neither individually or in combination part of the original input concepts. If a teacup, then the green striped object is likely a tea cozy with the white object adjacent, a tea pot. Although the information in this white paper and updates is intended to be current and accurate, the information presented therein may not reflect the most current technical- or procedural developments, regulatory actions or software developments.
Inclusion of any part of the content of this paper in another work, whether in printed or electronic, or other form, or inclusion of any part hereof in another web site by linking, framing, or otherwise without the express written permission of Gatfol is prohibited. At the same time, a great number of different sentence structures can have the same meaning. Thus a search for “chicken” might identify categories of “animals” and “recipes” and allow the analytics for instance to filter so as to only search within one of the two categories.
For example, if one of the words identified as semantically similar to “cool spring” was “refreshing” and one of the words identified as semantically similar to “spring water” was “liquid”, then “refreshing liquid” would be identified as semantically similar to “cool spring water”. This is done by looking up the word in the node word relationship database and checking whether the word following it appears within the list of row fields with more than a predetermined frequency.
Gatfol fluidly expands the “negative spaces” to contain almost any combination of concepts in an input dataset. Given these crystallizations, the yellow blob at back is likely not paint, but either jam, butter or coloured ice cream. Reports outline the tracking of weather systems, reports on response and recovery operations, as well as the damage, costs, and effects associated with emergencies and disasters by area. Using the reverse signature of that word, a check is also made to see whether the word preceding it appears within the list of row fields with more than a predetermined frequency. Given a “tea pot and cups” element grouping, the dark bands at back right is likely a chair structure, and given this, the light green object is semantically unlikely to be a picnic blanket or green meadow, but rather a table cloth. Will also include articles regarding requests for resources, disaster proclamations, and requests for assistance at the local, state, and federal levels.
Gatfol is not responsible for any errors or omissions in the content of this white paper or for damages arising from the use or performance of this white paper under any circumstances. Only if both the preceding and following words appear within the row of fields with more than a predetermined frequency is the word regarded as fitting grammatically within the string, otherwise they are rejected at the final stage. In this iterative manner a large volume of detailed tagging is obtained from a limited initial set of ambiguous descriptions. We encourage you to contact us for specific feedback- or advice as to your particular matter.



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