Journey from Gutenberg Printing Press to 2nd Order Probabilistic Semantics :- Thinking Web WW4 Achieving Medical Ontology for Medical Reasoning

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1. 1452, Printing Press Revolutionized Professional Knowledge Distribution

Johannes Gutenberg (c. 1398 – February 3, 1468) was a German blacksmith, goldsmith, printer, and publisher who invented the first printing press. Gutenberg’s printing press revolutionised the creation of books and helped make them affordable, ushering in a new era of affordable books and literature.
1452, with the aid of borrowed money, Gutenberg began his famous Bible project. Two hundred copies of the two-volume Gutenberg Bible were printed, a small number of which were printed on vellum. The expensive and beautiful Bibles were completed and sold at the 1455 Frankfurt Book Fair, and cost the equivalent of three years’ pay for the average clerk. Roughly fifty of all Gutenberg Bibles survive today.

The Gutenberg Bible is the first substantial book printed in the West with moveable metal type. Before its printing in 1454 or 1455, books were either copied by hand or printed from engraved wooden blocks—processes that could take months or years to complete. Johann Gutenberg invented a printing press that revolutionized the distribution of knowledge by making it possible to produce many copies of a work in a relatively short amount of time. Learn more about the Gutenberg Bible through the links below.

Printing Press:- By giving all scholars the same text to work from, it made progress in critical scholarship and science faster and more reliable.

2. WWW :- Introduced non-linear linking or information across systems.

In March 1989, Tim laid out his vision for what would become the Web in a document called “Information Management: A Proposal”.Believe it or not, Tim’s initial proposal was not immediately accepted. In fact, his boss at the time, Mike Sendall, noted the words “Vague but exciting” on the cover. The Web was never an official CERN project, but Mike managed to give Tim time to work on it in September 1990. He began work using a NeXT computer, one of Steve Jobs’ early products.

“In those days, there was different information on different computers, but you had to log on to different computers to get at it. Also, sometimes you had to learn a different program on each computer. Often it was just easier to go and ask people when they were having coffee…”, Tim says.

Tim thought he saw a way to solve this problem – one that he could see could also have much broader applications. Already, millions of computers were being connected together through the fast-developing Internet and Berners-Lee realized they could share information by exploiting an emerging technology called hypertext.

3.A – 1999, Tim Berners-Lee described the Semantic Web vision in the following terms

I have a dream for the Web [in which computers] become capable of analysing all the data on the Web, the content, links, and transactions between people and computers. A Semantic Web, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The intelligent agents people have touted for ages will finally materialise. (1999)

3.B – 1st Order Semantics :- Knowledge Engineering and the Web of Data – Harald Sack is Senior Researcher at the Hasso Plattner-Institute for IT-Systems Engineering (HPI) at the University of Potsdam

https://player.vimeo.com/external/95279410.hd.mp4?s=746c2b595f18bd581dcb5444db5901c61b259ae3&profile_id=113&oauth2_token_id=60919992

4. 2014 2nd Order Semantics :- Adding Probabilistic Semantics to better Reasoning employing Knowledge Engineering 

4.A – Bayesian Reasoning from The Uncertainty on Web

Paulo Cesar G. da Costa, Kathryn B. Laskey , Kenneth J. Laskey

Uncertainty is ubiquitous. If the Semantic Web vision [1] is to be realized, a sound and principled means of representing and reasoning with uncertainty will be required. Existing Semantic Web technologies lack this capability. Our broad objective is to address this shortcoming by developing a Bayesian framework for probabilistic ontologies and plausible reasoning services. As an initial step toward our objective, we introduce PR-OWL, a probabilistic extension to the Web ontology language OWL.

4.B :- 2014 – Dr. Barry Robson proposed using quantum mechanics as a basis for heuristics with the design and implementation of inference nets.

Split-Complex Numbers And Dirac Bra-Kets

Dr. Steven Deckelman ·  Dr. Barry Robson

Physical analogy and intuition has a long and distinguished tradition as a source of inspiration and deep mathematical insights. Take, for example, Jean Bernoulli’s ingenious solution to the brachistochrone problem, based on the path light takes through an inhomogeneous stratied medium as described in or the original solution of the Dirichlet problem based on physical reasoning for a physical electrical potential being determined by the laws of electrostatics given a charge distribution on the boundary. Or recall P.A.M. Dirac’s delta function  from the early days of quantum mechanics δ(x),  is a generalized function, or distribution, on the real number line that is zero everywhere except at zero, with an integral of one over the entire real line. Dirac delta function is a typical example of how a physicist’s unerring mathematical intuition can go beyond the level of the mathematics of his time. Or consider Wiener’s development of stochastic processes based on trying to model physical Brownian motion.We could, of course, go on and on. Recently Barry Robson has proposed using quantum mechanics as a basis for heuristics with the design and implementation of inference nets. The resulting net, called a Hyperbolic Dirac Net (HDN), is based on split-complex numbers. Inference nets, a topic in artificial intelligence, are very important in bioinformatics, data mining and biomedical analytics as well as having many other applications. An example from biomedical informatics would be a patient record database. The science of designing and implementing such nets in a computationally tractable way is a nontrivial problem in computer science.

Dr. Barry Robson

2014 :- Q-UEL – Probabilistic Semantic achieving, a WW4 for Medicine

Probabilistic Medical Ontology for Medical Reasoning by HDN Inference

4.B 1 Q-UEL The Dirac Notational Language :- To represent High Dimensional Data and Multi-lateral Subject

Q-UEL (Quantum – Universal Exchange Language), a web-based universal exchange and inference language for healthcare and biomedicine . It is extended to the more traditional domain of public health analysis including general population health sampling, healthcare quality surveys, and screenings. The techniques used can include or extend to cross-sectional studies, cohort studies, and other similar investigations including, to some extent, clinical trials.

4.B 2 HDN The Knowledge Model :- To represent 2nd Order Semantics

HDN – Medical Ontology for Medical Reasoning

1. Hyperbolic Dirac Net (HDN) developed for medical inference overcoming the limitation of acyclic modeling in Bayes Net (BN), which leads into Directed Acyclic Graph (DAG). Motivating being that, while the traditional Bayes Net (BN) is popular in medicine, it is not suited to that domain.
2. Medical domain, as such has many interdependencies, owing to high dimensionality of the data and multi-lateral nature of the subject, such that any “node” can be ultimately conditional upon itself.
3. A traditional BN is a directed acyclic graph by definition, while the HDN is a bidirectional general graph closer to a diffuse “field” of influence. Cycles require bidirectionality; the HDN uses a particular type of imaginary number from Dirac’s quantum mechanics to encode it. Comparison with the BN is made alongside a set of recipes for converting a given BN to an HDN, also adding cycles that do not usually require reiterative methods. This conversion is called the P-method. 

The obvious problem here is that any knowledge network prepared to realistically model the world must allow for cyclic pathways that can be traced through the connections (edges) of the network (graph). To the extent that we can think of nature as a graph at all, it must be a general graph, but because relationships can potentially exist between any node and every other node but present as matters of degree, it can be further objected that the ideal picture is not even a general graph with cycles, but something closer to a continuous field of interactions between things. This perception also relates to the employ of conditional probabilities.

Meaning as ubiquitously and diversely seen in street, highway, train and subway maps, family ―trees, biochemical pathways, schematics in medical student‘s lecture notes, flowcharts, Feynman diagrams and path integrals (important for HDN philosophy) and not least the concept of the Semantic Web. With the interesting exception of such as physical, chemical, biological and computational processes that necessarily imply a probabilistic basis for transitions, their ubiquitous and confident use is obviously due to the fact that the relationships are held as being certain, not probabilistic. Intuitive probability calculation can be highly misleading as discussed in this report, but it is hard even for the human brain to make a mess of estimating overall probability when all the multiplied probabilities are assumed to be 1.

HDN – Inference Nets to :-

1. “Observe” – mine data
2. “Evaluate” – compute a very large number of the more significant probabilities and render them as tags,
3. “Interpret” – use a proposed inference net as a query to search amongst the probabilities represented by those tags, but only looking for those relevant to complete the net and assign probabilities to it, assessing what is available, and seeing what can be substituted, and
4. “Decide” – compute the overall probability of the final inference net in order to make a decision

• Data Science
• Semantic Web
• Machine Learning