Plerophoria vs Information

The eastern way of thinking about information

8 minute read

Information

The origin of the word information reveals its use. Inform comes from the Latin verb informare, which literally means to give form, or to form an idea. Form is the mold, the container, that is used to give shape, to molten the content. When computer scientists were designing and constructing the first digital computer, at the same time digital information was given birth and shape. Modern computers operate with memory chips and those are mere containers of storing sequences of 0s and 1s.

Plērophoria

The above interpretation and use of the word information is typical of western culture way of thinking. Although it is convenvient to keep things in boxes, Eastern philosophers used to think differently. The ancient and modern Greek word for information is πληροφορία, which transliterates (plērophoria) from πλήρης (plērēs) “fully” and φέρω (phorein) frequentative of (pherein) to carry through.

It literally means “bears fully” or “conveys fully”. In modern Greek the word Πληροφορία is still in daily use and has the same meaning as the word information in English

Wikipedia, the Etymology section of the term Information was updated by user Healis with the quoted text above on the 13th of June 2014

This contrasting use and interpretation of the word plerophoria traces its roots back to Socrates, Plato, and Aristotle’s theory of semiosis. In this regard every word plays the role of a symbol, i.e. sign that can be interpreted to communicate information to the one decoding that specific type of sign. There is an intimate and inseperable connection of the signified, i.e. the concept whose meaning the interpretant attempts to decode, with the signifier, i.e. sign’s physical form such as the sound of a word.

Every bit of digital information, i.e. 0 or 1 assimilates this triadic relationship. Bits are symbols signified as true or false taking the form of input voltage (signifier). The infinite combination of sequences of such symbols gives us the power to represent anything digitally. Their meaning depends on how we interpret these sequences, as numbers, letters, sounds, color, or anything else that can be encoded. The form of the signifier is not limited to that of a mere container that stores a sequence of 0s and 1s. It is a fully functional level of abstraction connected to higher and lower levels by applying recursively the theory of semiosis until we reach CPU’s binary level.

The Turing Machine

One-tape Turing machine according to Hopcroft and Ullman can be formally defined as a 7-tuple. Elements of this tuple are members of three distinct sets. A non-empty set of tape alphabet symbols, e.g. {0, 1}, a non-empty set of states, e.g. {A, B, C, HALT} and a set of state transitions e.g. {L, R}. Turing machine reads the tape symbols and executes a sequence of instructions according to a state table. Although this is not the space and time to adapt the theory of semiosis on the Turing machine we can clearly see an analogy. The tape symbol plays the role of a sign, the signified instruction is executed according to the interpretation given by the state table, and there is the physical form it takes as a printed text symbol on a white square of a paper tape. You can apply the same logic to the set of states and the set of transitions because these are symbols (signs) too; they can be interpreted and realized in some other physical or non-physical form.

R3DM/S3DM Abstraction Mechanism

In digital representation of information we can define a chain of interpretations, representations and realizations that are built in a consecutive order. This chain of semiosis reveals the mechanism that we can use to build higher levels of abstraction. At each step the symbol that is used to link together the signifier with the signified can become a fundamental unit, i.e. signifier to build the next level of abstraction. Thus we can move in two directions, we can generalize or we can specialize.

Cross-References

R3DM Project Posts

2018

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TRIADB at Connected Data London
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2017

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European Wolfram Technology Conference 19-20 June 2017 in Amsterdam
My speech at European Wolfram Technology Conference 2017 about a new data modeling framework R3DM/S3DM that is implemented on top of OrientDB graph database and coded in Wolfram Mathematica

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An overview of critical points to consider when modeling with R3DM/S3DM
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Associative Data Modelling Demystified: Part 6/6
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Data Modelling Topologies of a Graph Database
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The associative data graph database model is still a heavy hitter, stacking up well against property graphs and triples/quadruples. Expect a comeback.

A Quick Guide on How to Prevail in the Graph Database Arena
A brief discussion on criteria to meet a differentiation strategy for graph databases
A swift introduction to the key factors that influence the performance and unification character of graph databases

Associative Data Modeling Demystified: Part 5/6
Qlik Associative Model
Qlik's competitive advantage over other BI tools is that it manages associations in memory at the engine level and not at the application level. Every data point in every field of a table is associated with every other data point anywhere in the entire schema.

2016

Associative Data Modeling Demystified: Part 4/6
Association in RDF Data Model
In this article we will see how we can define an association in RDF and what are the differences with other data models that we analyzed in previous posts of our series

Do you Understand Many-to-Many Relationships ?
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Associative Data Modeling Demystified: Part 3/6
Association in Property Graph Data Model
In this article, we continue our investigation with the Property Graph Data model. We discuss how a many-to-many relationship is represented and compare its structure in other data models

Associative Data Modeling Demystified: Part 2/6
Association in Topic Map Data Model
In this post, we demonstrate how Topic Map data model represents associations. In order to link the two, we continue with another SQL query from our relational database

Associative Data Modeling Demystified: Part 1/6
Relation, Relationship and Association
In this article, we introduce the concept of association from the perspective of Entity-Relationship (ER) data model and illustrate it with the modeling of a toy dataset

2015

Plerophoria vs Information
The ancient Greek origin of the word information
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We introduce the Atomic Information Resource (AIR) unit of R3DM conceptual framework

Towards a New Data Modelling Architecture
Part1 - Relational/ER Constructs in Wolfram Language
We start with terms and constructs that most of us are familiar with from the Relational and Entity-Relationship database management systems

2014

R3DM/S3DM Illustration and Formalization
Old wiki pages and LinkedIn posts ported from neurorganon.org and linkedin.com
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2013

R3DM in TMDM
Old wiki pages on R3DM ported from neurorganon.org, examples of R3DM in TMDM
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Old wiki pages on R3DM ported from neurorganon.org
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The problem of information resources definition, representation and identification
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2012

Ignite Athens 2012 : From WWW To GGG
My first pitch at Ignite Athens on 20th of September 2012 about Neurorganon Upper Level Ontology (NULON)
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