SOME ASPECTS OF DEFINING KNOWLEDGE (draft 15h July 2009)?

 

  K Abhary*, H K Adriansen**, F Begovac***, D Djukic****,

B Qin*****, ?S Spuzic* and K Xing*

* University of South Australia, **University of Aarhus, *** Zenica University,

****Massey University, *****Renmin University of China

 

 1. Intro

 The purpose of this treatise is to contribute to alleviating some problems that arise during the application and sharing of knowledge in a cross-disciplinary environment. An effort is made to integrate the relevant definitions with epistemological and didactic basics.

 Our knowledge has undergone a long history of evolution and we indeed continue with collating the further knowledge about our Globe, setting our sights at the outer space as well; at this stage we are aware of over 100 billion galaxies and each galaxy contains up to 400 billion stars. We are exploring the planets orbiting our star ─ the Sun, and the electrons orbiting the atomic nuclei. The corresponding knowledge repositories grow exponentially and the question is how to store, share, manage, combine and use this immense treasure.

Mankind’s knowledge is transferred from an individual to his contemporaries, from generation to generation and from civilisation to civilisation for at least several thousand years. But the history of the Man stretches much further back and there is a solid evidence of tools made well over a million years ago. A ‘tool’ is a phenomenon that significantly increases the probability of realisation of an intended (premeditated) change, providing that relevant definitions are used. Since a ‘definition’ is a substantial component , a ‘brick’ necessary  to construct and communicate the subject of knowledge, one could hypothesise that the first traces of knowledge are well over a million years old.

Knowledge records can be combined to have a central focus and to converge to similar classes of concepts following a hierarchy that serves to establishing a (scientific) discipline. The major motive for grouping theories and hypotheses into the scientific disciplines is to facilitate the storage, growth, communication and application of a specific category of knowledge. 

The academic educational streams (scholarly disciplines) are established with an additional motive to enable systematic learning, study and further extending the observed class of knowledge. 

There are numerous classifications of disciplines used in academe and beyond; Figure 1 presents an example of possible groupings.

 

Figure 1: An example of possible grouping of disciplinas

In the applications of knowledge e.g. in industry, sport and other spheres organised to manage methodically well recognized classes of our needs and interests, we often combine canons, theories and hypotheses taken from quite differing scientific disciplines. We create so called cross-disciplinary constructions of knowledge. Theories that have been validated in one discipline can help in resolving issues that are traditionally investigated within a completely different area.

 

A question is whether the existing, authoritatively established academic disciplines present the only possible arrangement of the repositories of the existing knowledge? Did these disciplines become the cages that obstruct efficient combining of available theories and hypotheses? Contemporary views such as constructivism and flexible education have inspired publications such as Basics of Manufacture that attempt to introduce cross-disciplinary knowledge based on a problem-oriented application of classical disciplines.

 

The major stumbling block to activating the synergy of such an approach is the fact that our knowledge has grown considerably faster than the language resources that are used to describe them.

 

The principal concepts needed to define knowledge are treated by numerous sciences, as outlined in Fig 2.

 

Figure 2: Academic disciplines concerned with the fundamental concepts of knowledge

 

The issue of the inconsistency in nomenclature of scientific and engineering terms used across the differing disciplines is becoming increasingly evident. 

 

Underlying causes for these misments include differences in interpretation of basic concepts such as ‘definition’, ‘knowledge’, ‘logistic’, ‘structure’, ‘element’ and ‘ontology'. Inconsistencies can be found in interpretations of important scientific and engineering terms such as ‘technology’ [10], ‘metal’ [22], ‘vector’ [29] or ‘variance’ [8] ─ the whole range of key terms are used by reputable sources to denote quite differing concepts. This can be largely ascribed to insufficient communication between the branches of science that are vigorously growing adrift. Root causes behind the obstacles such as homonymy and jargon lie in phenomena such as conflict of intents and mismatch of beliefs.

 

As an example, operations engineering has taken over the concept of ‘logistic’ by defining that: “’Logistic’ means of or pertaining to ‘logistics’”. (“’Logistics’ is here the management of the flow of goods, information and other resources, including energy and people, between the point of origin and the point of consumption in order to meet the requirements of consumers ─ frequently, and originally, military organisations. Logistics involve the integration of information, transportation, inventory, warehousing, material-handling, and packaging.”)

However, according to Max Black [30], this adjective (logistic) qualifies (characterises) “any theory which asserts that mathematics should be considered as a branch of logic”. To add to potential misconception, the same adjective is used in two-word terms such as ‘logistic distribution’ and ‘logistic regression’ in mathematical statistics.

Still, much broader and older (1918) use of this term is a noun ‘logistic’: “’Logistic’ is the science which deals with types of order as such. It is not so much a subject as a method. Although most logistic is either founded upon or makes large use of the principles of symbolic logic, still as science of order in general does not necessarily presuppose or begin with symbolic logic.” ([27] Lewis 1960: p. 3. and pp. 7-9).

Many sources point at the disturbing presence of ambiguous, inconsistent and overlapping concepts in the global community of sciences [5, 8-15, 20, 50, 56, 59, 62].

 

Misalignment of the basic concepts obstructs capitalising from the potential cognitive synergy that can be activated by integrating knowledge specified within "differing" disciplines. Therefore this discourse presents some basic concepts needed to define the structure of knowledge.

 

2. Knowledge

 

The minimum intent of the following definition of the concept of ‘knowledge’ is to present a reference (a metric, a comparator, or a norm) that must be observed when defining scientific and engineering concepts.

Knowledge is defined (Oxford English Dictionary) variously as (i) expertise, and skills acquired by a person through experience or education; the theoretical or practical understanding of a subject, (ii) what is known in a particular field or in total; facts and information or (iii) awareness or familiarity gained by experience of a fact or situation. Philosophical debates in general start with Plato's formulation of knowledge as "justified true belief". There is however no single agreed definition of knowledge presently, and there remain numerous competing theories. [42]

Knowledge acquisition involves complex cognitive processes: perception, learning, communication, association and reasoning. The term knowledge is also used to mean the confident understanding of a subject with the ability to use it for a specific purpose if appropriate. [42]

It appears that the concepts such as ‘Knowledge Engineering’ are used without a consistent definition and with certain dose of negligence of the above diversity. Differing sources use the same term ‘knowledge’ to address quite special and sometimes significantly differing aspects. This is not necessary because there is an entire spectrum of terms available for systematic characterisation of differing concepts, e.g.:

          Table 1

(i)

Knowledge

(viii)

Competence

(ii)

Noesis

(ix)

Proficiency

(iii)

Cognition

(x)

Discernment

(iv)

Expertise

(xi)

Noema [18, 19]

(v)

Expertness

(xii)

Understanding

(vi)

Competency

(xii)

Comprehension

(vii)

Aptitude

(xiii)

Cognisance

 

However, except for ‘noema’ all above listed terms are both homonymic and synonymic; in addition, the usages of pertinent concepts are marked by ambiguous scope and vague definitions.

 

‘Noema’ is used in medicine and epistemology to denote the state of the (part of the) mind of a single person, which is the result of person's intention to understand something, and consequently brings about her/his belief about relevant, perceived understanding. Difference between ‘noema’ and ‘thought’ is in that the ‘thought’ is not necessarily intended nor believed by the thinker. (Strictly speaking, Oxford English Dictionary quotes usage such as "'noema’ is obscure speech or speech that only yields meaning upon detailed reflection.”, referred to source Peacham (1577) “Garden of Eloquence”. It is proposed herewith to abandon this definition, i.e. noema = obscure speech, as obsolete.)

Misconceptions such as one “Knowledge cannot, as such, be stored in computers; it can only be stored in the human brain. … There is no knowledge without someone knowing it.”; [1], p 11) can certainly be avoided if this statement is rephrased as follows:

“Noema cannot, as such, be stored in computers; it can only be stored in the human brain; there is no noema without someone contemplating it.”

It is important to note the difference between the concepts of ‘noema’ and ‘knowledge’. A single person can make assumptions and study information, definitions, hypotheses and even a significant combination of several theories. By the process termed ‘noesis’ a single person arrives in his mind to ‘noema’ about anything. ‘Noema’ is a special case of, somewhat relative knowledge achieved at the specific point in time by a single person. Without the noesis, consciousness would be unconscious (conscious of nothing) [24].

On the other hand, knowledge is established as a system of relations shared by more than one person, usually a significant number of humans. Knowledge can continue to exist over huge time-span with a considerable reliability. At a higher level, knowledge is stored systematically as an asset within the scientific disciplines and it is accessed and used for multiple purposes by an individual, by a group and by the broadest society.

By adopting this differentiation between the concepts of ‘knowledge’ and ‘noema’ many important relations in knowledge management can be studied and understood more exactly. For example the concept of ‘noesis’ is used to adapt metamodeling strategy for more efficient developing of information systems [60].

An individual person can reach high-level noemas, and this state could be maintained over a significant, yet finite period of time. However, the synergy achieved by transforming noemas into shared knowledge offers incomparably larger potentials. In order to take the highest degree of advantages of the available knowledge the group must organise, share and communicate the relevant knowledge at an appropriate level. The level at which the relevant knowledge is structured, presented (explained), stored, communicated, compared and shared determines the eventual usefulness of knowledge.

Terms (i) to (iii) above are mutually both synonymic and homonymic, [16, 17]. All three terms are used to denote quite complex concepts discussed in a number of theories within several scientific disciplines. It appears that the first concept (knowledge) is used in relation to an achieved state, while the concepts (ii) and (iii) emphasise the developing (growing) stages.

Terms (iv) to (ix), used to emphasise more specific domains and particular applications, are also both synonymic and homonymic. These concepts are often used with reference to a person who is an expert (competent, proficient). However, the usages of these concepts lack systematic classification, and there is no consensus about precise definitions. Thus for example, information sciences use concept of ‘expert systems’ (also known as a ‘knowledge based system’) to address applications of artificial intelligence tools (‘computer programs’) to store, search, retrieve and use the knowledge and analytical skills emulating human experts, related to a specific subject. Term ‘expertness’ is popular in psychology, while its synonym ‘expertise’ is used in technology and organisational sciences.

Remaining group of four terms (x to xiii) are also mutually synonymous, at least to some extent. It seems rational to consider ordering these concepts to reflect the hierarchy in growing complexity e.g. ranging from perception to contemplation.

Since this state of the art is objectionable, in the following sections an attempt is made to present the basic concepts that can be used to construct the knowledge stratum in a more orderly manner. To this end, the first approximation to analysing the structure of the knowledge is presented below with the minimum intent to make it more transparent. In this simplified model, knowledge structure is visualised as a stratum composed of ‘assumptions’, ‘definitions’, ‘hypotheses’, ‘theories’, 'canons' and ‘disciplines’.

It is useful at this point to take in consideration hypothesis that the non-man-made systems (e.g.: microflora, solar system, inner core of the Earth) and the entire ambient are continuously undergoing some changes, and although there are many ‘closed routes’, these ‘cyclic processes’ are also subject to incessant changes at varying rates and extents. The range of these variations has a span of galactic proportions.

Some phenomena (including man-made systems, for example a theory, a chemophysical system, etc) can be brought into conditions that resist to this restless ambient, at least to some extent. In the case of man-made systems, this ‘constancy’ is sustained by activating various man-made (man-initiated) processes. Consider the following two examples: First one is a manufacturing process where a sophisticated control and maintenance  sustain the attributes of the final product within the predefined tolerances. Some products such as bread, dishes or shirts have been produced for over two millennia.  Second example is a theory that was well presented and verified in several disciplines and in many practical applications. Some theories have resisted to changes for more than two millennia, for example Pythagoras’s Theorem, and it is anticipated that this (and a number of other theories) will continue to exist unchanged.

2.2. Definition

 

The minimum intent

The minimum intent of the following definition of term ‘definition’ is to present a reference (metric, comparator, norm) that must be observed when defining scientific and engineering concepts.

Axioms

 

Axioms are initial assumptions. It is impossible to analyse anything without adopting several non contradicting points ─ axioms. Axiom is a proposition that is not susceptible of proof or disproof; its truth is assumed to be self-evident on its own merit. However, axioms can be changed, and replaced if this is done consistently for the whole derived analysis.

It is useful to start with a quite general concept—‘something’—which can mean anything (but it does not automatically include ‘everything’).

 

‘Universe’ embraces everything in its uttermost totality.


The concept of ‘ambient’ is used for everything in the vicinity of, and, to a certain degree within, something. ‘Ambient’ is a fraction of the ‘universe’.


‘Process’ refers to something that can be distinguished from its ambient. When the rates of changes are significantly differing in one process relative to the other, the ‘slow’ process is frequently termed ‘event’.


‘Relation’ involves two or more processes (events), and ‘system’ is something constituted by two or more relations.


‘Phenomenon’ is a generic concept (hypernym) for the above terms, providing that at least one human sense indicates (directly or indirectly) its existence. This leaves open a possibility that in our ambient may exist something we did not observe (detect, sense) thus far. Our ambient is constituted of phenomena and other processes, relations and systems.

 

It is assumed that all definienses used in this treatise are already intrinsically known. It is also assumed that definienses are not homonymous nor synonymous, nor do they contradict each other.

Definition


‘Definition’ is a fixed  (posited) set of relations that significantly increase the probability P of an intended (planned), observed, or anticipated, realisation (realisation = actualisation). When intended, such actualisation is to be achieved by a system (not necessarily manned) that can be organised to utilise a definition for such a specified purpose. When this realisation affects some phenomenon, the probability P can be estimated quantitatively, in addition to derivation of other quantitative measures such as entropy. A definition cannot be generated (created) without the existence of a manned system (e.g. a department within a socio-economic institution) which is organised and structured above a certain level of chaos. However, once it is generated and recorded, a definition can continue to exist (to be recorded, stored) without the existence of the initial manned system. When conceived by a relevant system, a definition becomes autonomous from its own representation (imprint, record); definition can be distinguished from any substance of which its record is made. Therefore, an identical definition can be replicated endlessly; it is infinitely shareable.

It appears that, if the probability P ≈ 0.5, the relevant concept can be deemed to be ambiguous, while with P converging to zero it seems only logical to doubt the validity of its definition. As the value of P approaches 1 (one) the validity of definition increases. Numerous deterministic concepts assume a maximum value of P, e.g. the probability of

a + b = b + a

is P = 1, for all numbers known thus far.

Reliable definitions (with P significantly higher than 0.5) can be used to derive new hypotheses by means of logical deductions and inductions. By subsequent collating significant evidence, so formed hypotheses can be converted into new definitions.


Definition is composed of inter-related components: informations and data. The attribute ‘fixed’ (‘posited’) in the above context emphasizes the difference between the restless process and the permanent information. In other words, although our ambient is in the state of perpetual motion, a definition—a model—can be generated, not to imply that the defined phenomenon is at a standstill, but to create a specific unchanging metric (index); unchanging until useless.


In other words: a definition is a probability intensifier for an anticipated or detected (observed) realisation (actualisation, objectivisation). Each definition is unalterable, terminable, infinitely (and simultaneously) shareable, and it does not contradict to, or otherwise deny any other definition.

Phenomena change in various manners at very differing rates (‘speeds’). These differences range at the galactic proportions. In terms of a definition, this that can be perceived as continuous or incremental processes, ranging from quantum leaps to comparably momentous discrete phenomena. Collating informations and data can be perceived as relatively continuous process of making ‘snap shots’ (instantaneous records), while establishing a definition is a radical final stage in this nucleation. It is important to comprehend that the ‘definition’ is a static, unchanging relation ─ a theoretical (abstract) reference (an index point). Depending on the rate of the observed phenomenon, information can be perceived to be related to an ‘event’ or a ‘process’. An event can be viewed as an identifyable stage of some process; usually the rate of change of such stage is considerably lower compared to the rate of changes of other relevant processes. Similar contemplation can be made at the higher level: a theory or a hypothesis, both are the resulting stages ─ systems that constitute a final construct. Establishment of a theory and a hypothesis is marked by an evident transformation from one to another level of knowledge.

One way of comprehending the above ideas is to visualise the following analogy: an information is an ‘intellectual photograph’ of a phenomenon. Following this illustration, a definition can be depicted as a photo album, while the theory can be compared to the whole library of photo albums. The whole comparison can be extended to ‘movie clips’ (animation records) where the entire dynamic sequence of a process is recorded and it can be replayed for many viewers.

Any theory and any definition are limited by their assumptions and the scope of applicability. Although many hypotheses can be derived from a well established theory, reality is that there will always be possible to extend explorations and make further contributions to knowledge. In addition, many hypotheses have been proven erroneous, and numerous theories and definitions had to be modified if not abandoned. This process would be impossible if an incremental evidence of concepts is not established with fixed definitions that serve as a reference for further development.

A definition should be complemented with a ‘statement of minimum intent’ which specifies a minimum domain of purposes for which this definition can be used. This statement does not exclude a possibility of using the same definition correctly for some other purposes. However, any extended use must not violate (contradict) already-established meanings (e.g. it must not cause synonymy or homonymy). In addition, a definition must be complemented with axioms, with one or more examples, and, when needed and possible, with audio-visual presentations. Multimedia are ideal tools for illuminating complex concepts. It is worth noting that the information media can be mutually translated, i.e. visual information can be translated into information received by tactile, hearing and other senses.

A definition should also include both hypernym(s) and hyponym(s). Including holonym(s), meronym(s), antonym(s), etc is recommended, but may be omitted when it causes prolixity.

 

Let us present an example of a ‘definition’ (the axioms are identical to the above listed axioms). A ‘tool’ is a phenomenon that significantly increases the probability of realisation of an intended (premeditated) change, providing that relevant definitions are used; e.g. a hammer, a pen, etc. An example of using a tool is the use of a hammer to make a wooden fence. A hypernym for a ‘tool’  is a ‘resource’. An example of a hyponym is ‘dishware’; the holonym is ‘equipment’. An example of a meronym is ‘tool handle’, and an antonym is ‘garbage’ (‘scrap’).

  

Figure 3: A hammer

Existing definitions can be used to infer new definitions, providing that the experiments confirm inferences.

Definitions are necessary components needed to construct and communicate the subject of knowledge. A definition is built by means of its structural elements: pieces of information. Information and data are built by virtue of construction bits, signals of various kinds. These signals are combined in various manners; one of most frequently used combinations includes the ‘terms’.

It is hereby recommended to avoid using the term ‘definition’ to denote the following concept: The fidelity (accuracy) with which detail is reproduced by a television or video display system ranging from fuzzy to sharp appearance (also called ‘resolution’). [31]

When addressing the accuracy (fidelity, resolution) with which an electronic system reproduces the image based on its input signal (sharpness of an image as seen by the visibility of detail, clarity of outline), in computer science appears the need for introducing the term for “the number of pixels per square inch on a computer-generated display (the greater the number of pixels, the clearer the picture)”. Reputable sources such as Elsevier Inc. distribute scientific publications (e.g. [32]) where the above concept is denoted by terms such as ‘high-definition television’, ‘standard definition’ etc. This practice is however misleading since it implies that the ‘high-definition’ device (screen) provides automatically more knowledge than a ‘standard definition’ screen. However this is not the case. Firstly, the screen may display a completely false information, and in such a case the sharper display delivers most likely the more confusing picture. Secondly, in many cases the resolution of knowledge record does not affect the substance of actual theory conveyed. For example sharpness of the font used to present some alphanumeric paragraph is useful only to a certain degree; increasing the resolution beyond that threshold does not help in improving the actual content. Therefore the concept of ‘definition’ is too high a category and it should not be used to denote the ‘resolution’ (‘sharpness’, ‘clarity’, ‘fidelity’) of a computer monitor, television screen or a display generated by some other electronic device.

Other sources [32, 35] allocate to term ‘definition’ a whole range of interpretations. In some cases, such as ‘dictionary definitions’ [35] the significance of the function (purpose, intent) of definition is indicated. In other cases (such as ‘recursive definitions’ and ‘genetic definition’) sources [42 and 45] address variety of issues such the development history and method of defining, but fail to point at the significance of the relation between the ‘minimum intent’ of the definition and its content.

Concept of definition proposed in this treatise is applicable to each of the cases listed in sources such as [42 and 45], and therefore presents a hypernym for this variety.

For special “type” ─ ‘stipulative definition’ ─ sources [42 and 45] suggest that “A stipulative definition of a term carries a meaning which a speaker wants it to convey for the purpose of his or her discourse. Thus, the term may be new, or a stipulative definition may prescribe a new meaning to a term which is already in use.” Allocating new meaning to already existing term is bad practice which leads to unnecessary homonymy. It is more appropriate to coin new term for each new meaning.

2.3. Theory, Hypothesis and Knowledge

A system of inter-related definitions constitutes a theory.

An assumption that our ambient comprises also something yet to be defined, and/or is difficult to be detected by our senses, allows for generating unlimited number of further assumptions. As long as they do not contradict themselves, they can be combined to create hypotheses. Assumptions and hypotheses are important, because they present initial approximations needed for generating new definitions and theories. A complementary way of generating theories is by defining phenomena observed in our ambient.

In this presented transcript, it is proposed that the knowledge is a construct formed by interlinking a spectrum of intellectual components, the simplest entities being termed ‘information’. Information is composed of yet a simpler form, termed ‘data’. ‘Data’ (which can be further decomposed into ‘signals’) are tentatively positioned at the boundary of knowledge entities. There is a broad variety of levels expanding between the information layer and the highest strata of knowledge; several convenient concepts across this span are introduced as ‘assumption’, ‘definition’, ‘hypothesis’, ‘theory’, ‘canon’ and ‘disciplinae’. ‘Disciplinae’ is here defined as a subset of knowledge ─ a scientific or other knowledge encompassing a domain (an area, a field) of knowledge that is for some reason distinguishable from other knowledge.

One way of depicting this structure is shown in Fig 4.

Fig 4: One way of depicting the structure of knowledge strata

 

 As another example, an information that a simple mechanical lever is made of a carbon steel comprises data such as chemical content of that steel (e.g. 0.2 % C, 0.5 % Mn, 0.8 % Si etc) and lever dimensions. Such information contributes to constituting a definition only when the mechanical principles of lever operation are explained along with giving an example of application.

Furthermore, through combining this definition with other related definitions, such as moment of force, a theory of simple mechanisms (machines) is constructed. By amalgamating and broadening this theory with the complementary hypotheses and theories such as elasticity, statics or dynamics this system grows into branch called mechanics of solids which is related along with other branches to the more general disciplinae of mechanics.

“A ‘hypothesis’ is a tentative insight into the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena (a scientific hypothesis that survives experimental testing becomes a scientific theory)” [16]. For example we know many facts about the electromagnetic force, but at this stage we can only hypothesise about the remaining details as to why there is an attraction force between an electron and a proton.

Knowledge is constituted by system of inter-related theories and hypotheses. They can be combined as to have a central focus and to converge to similar classes of concepts following a hierarchy that serves to establishing a (scientific) disciplinae. Major motive for grouping theories and hypotheses into the scientific disciplines is to facilitate storing, growth, communication and application of specific category of knowledge. A disciplinae is a specialized structure of knowledge constructed by combining pertinent theories and hypotheses which together constitute the informational content of a systematic endeavour to explain (some part of) the universe.

Following the analogy with knowledge disciplinas (shown in Fig 1 above), the academic educational streams (scholarly disciplines) are established with an additional motive to enable systematic learning, study and also further extending the observed class of knowledge. Each of the above major categories can be further branched in several ways, e.g.

Table 2

Humanities

Earth

Medicine

- Sociology

- Psychology

- Philosophy

- Anthropology

- History

- Entertainment

- Sport

- Religion

- Languages

- Arts

- Multimedia

- Geography

- Geology

- Mineralogy

- Hydrology

- Vulcanology

- Geophysics

- Environmental geology

- Anatomy

- Cytology

- Embryology

- Epidemiology

- Genetics

- Histology

- Neurology

- Nutrition

- Pathology

- Pharmacology

- Physiology

Existence of broad variety in classification of academic disciplines is not necessarily a sign of disorder and misalignment. Academic disciplines and departments are structured depending on the needs of particular socio-geographic and economic regions. For example the science of Metallurgy can be traced as far as 16th century when  Georg Agricola wrote a book titled “De re metallica” and the academic discipline of  Metallurgy emerged at an advanced stage of establishing the "Universitas Scientiarum Technicarum" in dynamically industrialised  Europe. The discipline and academic schools of Metallurgy were maintained with a strong independent portfolio till mid 20th century due to the importance of industries based on metallic materials. By the end of 20th century the significance of other engineering materials imposed the gradual assimilation of schools and departments of Metallurgy under the umbrella of a more general discipline: the Materials Science.

Various amalgams of other disciplines are blended due to the motives of analogous category, but also due to intrinsic propensity of particular academic structures to grow along the growing branches of knowledge. For example, the early Geometry was a collection of empirical principles concerning lengths, angles, areas, and volumes, which were developed to meet the needs in surveying and construction. Since the evolution of Euclidean and non-Euclidean Geometry, the initial concepts have undergone a radical transformation. Contemporary Geometry considers manifolds that are considerably more abstract than the familiar Euclidean space. Modern Geometry is the base for Homotopy and has multiple strong bonds with Physics, exemplified by the ties between Riemannian Geometry and General Relativity. One of the youngest physical theories, String Theory, is also very geometric in flavour. [42]

The visual nature of geometry makes it initially more accessible than other parts of mathematics, such as algebra or number theory. However, the geometric language is also used in contexts that are far removed from its traditional, Euclidean provenance, for example, in fractal geometry, and especially in algebraic geometry.[42]

Mineralogy was historically a subdiscipline of Geology, but the mineralogists of the Space Age are interested in investigating samples from the Moon and Mars, notwithstanding the asteroids coming out of the depths of the Outer Space. Moreover, Mineralogy is one of the fields of utmost importance in Materials Science.

Information sciences present quite educative case of cross-disciplinary interactions. For example, at the Cornell University, the study of Information Systems draws from Computer Science and Operations Research; Human Computer Interaction from Communication, Psychology, and Cognitive Studies; Social Studies of Computing from Science & Technology Studies, Law, and Economics, which altogether constitutes a specific network of interacting disciplinas (Fig 5). [4]





Fig 5: Human-computer interactions according to a conceptualization used at the Cornell University [4]

 

3. Examples of symptoms, diagnoses and remedies

 

3.1. Some exemplary symptoms

 

Along with the above discussed concerns related to the concepts of ‘definition’, ‘logistic’ and ‘knowledge’, the published sources provide abundance of examples of misconceptions related to important terms such as ‘ontology’, ‘information technology’, ‘structure’, and ‘element’.

 

Before discussing the above terms, it is believed that pointing at one particular aspect of coining the terms is instructional in this context, namely the role of suffix ‘-logy’ in the structure of words.

 

Suffix ‘-logy’

 

The English suffix ‘-logy’ or ‘-ology’ denotes a field of study or academic disciplinae, and

‘-ologist’ denotes a person who studies that field. The suffix ‘-ology’ is a back-formation from the names of these disciplinas.

 

Some terms constructed with this suffix are used for concepts representing socio-economic, scientific and other speciality activities and systems.

 

In addition, evolution from Greek & Latin root (logia = speaking) resulted in terms denoting special language phenomena. [42]

 

The following Table 3 presents several examples from each usage:

 

Table 3

Academic Disciplinae

System of Activities

Speech Phenomenon

Technology

Technology

Analogy

Methodology

Methodology

Tautology

Pathology

Pathology

Haplology

Radiology

Radiology

Eulogy

Biology

Ideology

Phraseology

Geology

Chronology

Trilogy

Mineralogy

Tocology

Neology

 

This inconsistency is confusing and it should be avoided, especially in the case of homonymy that can lead to misinterpretation of important concepts such as ‘technology’ [10].

 

Amongst the problematics of epistemology, none is more deeply rooted in evolution of knowledge than the intuition that ‘technology’ is in its true sense the oldest disciplinae.

 

Homonymous usage of the concept of ‘technology’ is notorious especially in contemporary engineering and scientific publications. It appears that numerous sources use the concept of ‘technology’ to address a (system of) ‘technique(s)’. Equally often ‘Technology’ is defined as science (study) of techniques, including the study of relevant resources, e.g. the study of tools, materials, other matter forms (such as electromagnetic radiation), equipments and other assets. [10]

 

It is also educative to observe how this inconsistency evolved in the case of contemporary progressive disciplinae termed ‘Information Technology’.

 

Information Technology, Computers and Informatics

 

‘Information Technology’ (IT), as defined by the Information Technology Association of America is “the study, design, development, implementation, support or management of computer-based information systems, particularly software applications and computer hardware. IT combines computer and communications technologies and deals with the use of electronic computers and computer software to convert, store, protect, process, transmit, and securely retrieve information.” [42]

 

At present, there is no longer any area in which the academic and other scientific research institutions are not supported by computers and pertinent software; indeed, an enormous variety of computerised aids is penetrating in one or another way the remaining aspects of contemporary civilisation.

 

The term ‘information technology’ is used to address the whole spectrum of computing and technology aspects, such as software design, data management, computer system administration, designing and maintaining computer networks and computer hardware.

 

The core tools in this field are devices termed ‘data (information) processors’ (Fig 6), in particular those based on electronic configurations.

Fig 6: Scheme presenting an information processor [49]



An information processor is made up of four basic parts: input, processor, memory (storage), and output. These devices consist of miniature electronic circuits that are combined into enormous variety of integrated systems commonly termed ‘computers’. Information processors are capable of recording information (a set of enumerated data) and transform it into another form, following a series of embedded algorithms (instructions), commonly termed ‘computer software’.

Term ‘Computer’ itself is a neologism influenced by history of information processing automata. First information processors were simple calculators which are further developed into sophisticated tools capable of following algorithms that enable performing complex mathematical operations. Although contemporary designs of computers literally use computing functions due to the digital nature of information processing, the term ‘computer’ is still misleading, and it is recommended to coin new term to address this advanced generation of information processing devices.

 

More complex algorithms make it possible for information processors to store and execute instructions called programs which make computers extremely versatile. Simple versions may be made small enough to fit into a pocket. ‘Personal computers’ are most familiar in public, however, the most common in use are so-called ‘embedded computers’ applied to control other devices, for example, they may be found in cars, TV sets, aircrafts, industrial robots etc.

 

At the same time, numerous other sources use terms that range from ‘Informing Sciences’ to ‘Computer Sciences’ to denote the corresponding studies and disciplinas.

 

In the light of the above, it should be appreciated that a consistent interpretation of the term ‘information technology’ would be: ‘the science of techniques, tools and other non-human resources used for processing information’.

 

The above listed tools, means and resources and applications (e.g. computer software, hardware, networks including the internet) can be associated with the concept termed ‘informatics’. ‘Informatics’ comprises ‘information engineering’ and ‘information systems’ (in analogy to concepts such as ‘data engineering’, ‘knowledge engineering’ and ‘method engineering’ [20, 50-54]). Therefore the ‘information technology’ is the science of ‘informatics’.

 

At this point it is educative to note that there seems to be a trend in the information and computer sciences to simply take over the terms that are already established in other disciplinas, and to allocate to these terms new meanings [10].

 

An example is a construction ‘data mining’ used in contexts such as knowledge-discovery and information systems. ‘Mining’ is one of the oldest scholarly terms known in the history of mankind, and it deserves to be preserved because of its roots and role in the history, the present, and the future of technology. As for the need for this term in Information Technology, why not use terms such as ‘data retrieval’, ‘data analysis’ or ‘data search’? [10]

 

Further example of synonymy and ambiguity are terms such as ‘electronic’ and ‘digital’ publications, forms, media and sources (that utilise electronics for the audience to access the content). Other European languages have far ago adopted a more convenient term ‘digitronic format (publication, media, source)’.

 

Ontology

 

Sources [11, 42, 46-47, 50, 62] point at the homonymous use of term ‘ontology’:

 

(i)  - Ontology is the science of (investigation into what) types of things there are in the universe (world) and what relations these things bear to one another. It studies being or existence and their basic categories and relationships, to determine what entities and what types of entities exist. [42]

- Ontology is the study of all forms and modes of being, the study of what there is, what exists in our ambient, what the stuff is reality made out off, what the most general features and relations of these things are (in the broadest  sense). An axiom of ontology states that the universe exists and that there are some things within that universe, that can be examined and mutually compared. [46, 61]

- Ontology is a philosophical discipline that encompasses (besides the study of what there is and the study of the general features of what there is) also the study of what is involved in settling questions about what there is in general (e.g., how to decide whether ‘time’, ‘beauty’ or ‘number’ exist). [46]

 

(ii)  - In both computer science and information science, an ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to define the domain. [42, 50, 53, 55];

- Ontology is a specification of a conceptualisation. That is, an ontology is a description of the concepts and their relations. What is important is what an ontology is for. Ontological vocabulary (lexis) is designed for the purpose of enabling knowledge defining, sharing and reuse; it implies definitions of terms (words) and concepts (e.g. figures) for the purpose of constructing and understanding theories and hypotheses. [11, 47]

-  Ontology is a formal model that uses mathematical logic to clarify and define concepts and relationships within a domain of interest (e.g. behavioral ecology) [59].

 

Concept introduced under (i) has a fundamental significance that must not be neglected in other disciplinas. For example, decision making is one of the strategic problems in ‘management sciences’; here the question of how to decide whether some aspect of a problem in fact does exists (or is just a subjective impression) can be answered based on the principles of ontology. For example, a stochastic approach can be employed to hypothesise about the significance of particular collated evidence.

 

There is no need for spreading the homonymy by disseminating the definitions presented above under (ii). Homonymy has an intrinsic propensity to multiply [10], for example authors in [55] already promote the whole range of ‘ontological structures’ such as ‘domain ontology’, ‘task ontology’, ‘application ontology’, ‘description & situations ontology’, etc.

 

It is recommended to coin a new term for the meaning under (ii) above, if the concepts such as ‘taxonomy’, ‘metamodeling’ or ‘nomenclature’ are found to be inadequate by the information and computer scientists and engineers.

 

Structure, texture, morphology, topology and topography

 

The concepts of ‘texture’, ‘topography’ and ‘morphology’ are frequently used within a common context with the concept of ‘structure’. This makes the basic notion of the concept of ‘structure’ rather ambiguous especially when taking in account that the term ‘structure’ itself is perplexed by notorious homonymy.

 

Concept of ‘structure’ is often used in materials science to address both the geometric topography and a variety of other chemo-physical phenomena within the solids. Furthermore, the forms such as ‘microstructure’, ’macrostructure’ and ‘nanostructure’ are frequently used in a wide range of scientific publications addressing physics of solids and related disciplinas. [37, 38].

 

As another example, source [36] discusses the ‘topology, texture, and microtexture’ of solid deposited layers and the “formation of … morphologies” all in the context of material ‘(micro)structure’. Authors even provide their own (presumably ‘stipulative’) definitions: “In the following discussion, ‘structure’ is used to describe the crystallographic order, within perfect (i.e. coherent) material stacks…, while ‘texture’ (macro- or micro-) is used to describe the overall relative orientation of material stacks (e.g., concentric, random, …)”.

 

Another source [39] defines ‘microstructure’ as “the size and distribution of individual grains/phases/defects in a (solid) material (as an example, the microstructure of sandstone consists of quartz grains in a calcite matrix)” with a further clarification that a ‘microstructure’ is “the structure of polished and etched solid as revealed by a microscope at a magnification greater than 25-50 times”.

 

To add to the overall confusion, terms ‘structural sections’, ‘structural steel’ and ‘structural engineering’ are frequently used within the context of civil and mechanical engineering to denote quite differing concepts.

 

Actually the concept ‘structure’ is appropriately defined as follows: “structure of something is which and how its parts (components, elements) are put together”. This is a very generic definition. For example, the structure of an atom can be defined by electrons, protons and neutrons, and their mutual relations. Bridge structure is composed of steel beams, columns and other mechanical components, while the structure of a literary publication can be composed of chapters and sections. In geology, we talk about the structure of the Earth. We explain what is the structure of something, depending of what is the purpose (intent) of this definition.

 

According to [40] ‘morphology’ is “the science of form or structure”. Other authors [41] use term ‘morphology’ when defining the inner structure of solids. Another source [16] defines morphology as “the studies of the rules for forming admissible words”, and also as “the branch of biology that deals with the structure of animals and plants”.

 

It is proposed hereby to adopt the following concept of ‘morphology’: ‘morphology’ is 3-dimensional topology applied to purely geometric features of solids.

 

The concept of ‘texture’ is defined in [16] as “the characteristic appearance of a surface having a tactile quality”, while in [42] “texture is the distribution of crystallographic orientations of a sample in materials science; a sample in which these orientations are fully random is said to have no texture.” The above interpretations indicate somewhat special aspect of the inner structure of solids – namely the prominent orientation in one of directions relative to the overall (external) geometry of a solid. It holds that morphology would include study of phenomena such as eventual (absence of) texture as well.

 

It is recommended to avoid interpretations such as “In music, texture is the overall quality of sound of a piece, most often indicated by the number of voices in the music and by the relationship between these voices” [42].

 

Concepts of ‘topology’ and ‘topography’ are also burdened with homonymy. For the sake of brevity, only the recommendable versions will be presented below:

 

Topology is a branch of study of mathematics, an extension of the study of geometry. It examines the attributes of objects that do not change when the object is distorted. For example, in topology two objects are considered to be the same if each one can be distorted to the other without being cut or torn. A typical example is a map of a railway system showing the railway lines and how they connect in very simplified geometric form. An accurate map of the same railway system would have lots of bends and uneven spacing. The simplified map is topologically equivalent to an accurate map. The important information, like the order of stops and how the different train lines are connected, does not change as the map is distorted from one to the other. [16, 42]

 

Behind the above concept is an understanding that the objects in the space have certain structure, topography, geometry and other attributes thus allowing for classification into sets that can be analysed based on selected features. The disciplinae of topology further expands into topics such as

- point-set topology, which investigates such concepts as compactness, connectedness, and countability;

- algebraic topology, which investigates such concepts as homotopy and homology; and

- geometric topology, which studies manifolds (e.g. objects such as Möbius strip) and their embeddings, including knot theory.

 

(Concept of ‘topology’ is additionally discussed in Appendix).

 

It is proposed hereby to define ‘topography’ as “the significant relations that define a limited class of boundary surface aspects of a phenomenon”. ‘Significant relations’ are those which affect realisation of an intent involving the relevant phenomenon. ‘Boundary surface aspects’ are related towards the ‘outer surface’ rather than towards the ‘internal volume’ of a phenomenon. A typical (and historical) example of topographic information are geographic maps. Geographic maps reveal the topography, i.e. the landscape or the terrain usually by employing different colours. Often maps reveal a combination of topographic and political features by showing both mountains and country borders.

 

In summary, concepts of topography, structure and texture are relations constituted at deferring levels of generality.

 

- Topography is related to most general class of boundary surface phenomena with regard to selected common aspects.

- Structure points at relations within a phenomenon.

- Texture indicates a special case involving the (evidence of) geometric orientation of structural components within solids.

 

Topology and morphology are sub-disciplines of mathematical sciences.

 

- Topology is study of selected relations (transformations) involving topography and structure.

- Morphology is 3-dimensional topology applied to purely geometric features of entities.

 

Element

 

UNESCO sources [33-35] distinguish three descriptors:

- chemical element,

- structural elements (buildings),

- trace elements,

 

and three additional usage phrases:

- elementary particles,

- elementary schools,

- elementary education.

 

It is certainly not recommend use of formulations such as ‘Chemical elements such as Uranium, can damage structural elements, due to long-term emission of elementary particles, even when present as trace elements’, especially not in the elementary education.

 

The WordNet [16] provides 7 options. The most differing meanings include:

a) ‘Element’ (n) is any of the more than 100 known substances (of which 92 occur naturally) that cannot be separated into simpler substances and that singly or in combination constitute all matter

b) ‘Element’ (n) is a component, constituent, an artifact that is one of the individual parts of which a composite entity is made up; especially a part that can be separated from or attached to a system, e.g. "spare element for cars".

 

There are further homonymic usages not recommended hereby, such as “‘element’ is the most favourable ambient for a plant or animal; e.g. water is the element of fishes".

 

The above definition cited under (a) is promoted herewith as the preferable use for this term. Use presented under (b) above, can be substituted by terms such as ‘part’, ‘component’ or ‘constituent’. UNESCO descriptors, such as ‘chemical element’ and ‘structural element’ are too lengthy, while the phrases ‘elementary particles’ (why not: ‘sub-atomic particles’?) and ‘elementary education (schools)’ (why not: ‘primary education’?) are too ambiguous.

 

3.2. Efficiency of manned systems

 

Manned system is a group of (few or many) people who work together to pursue common goals, and use collective assets. An appropriate understanding of a ‘common goal’ requires acknowledging a variety of aspects within an interactive structure that includes detailed and individual perceptions, intentions and beliefs. Given other the same two manned systems can perform quite differently depending on how they communicate knowledge, and it is pointed out hereby at the significance of interaction of sharing

  i.         knowledge,

 ii.        intents and

 iii.         beliefs.

 

The released synergy is proportional to the completeness and concordance of all three above aspects.

 

A typical example is an academic institution which rejected collaboration offered by peer institution due to belief that it is more important to protect its market. However the loss of the market occurred in fact due to the deterioration in the quality and versatility of the offered academic services.

 

Another example is a large industrial system that suffered significant financial losses due to the lack of dialogue between two antipode expert teams each protecting their territory of knowledge.

 

Not rarely, the intellectual protection and confidentiality safeguards resulted in the loss of the expertise due to the lack of knowledge sharing [56]. There are known cases of world-leaders in industrial engineering that employ quite aged experts because they are the only sources of relevant knowledge. The critical moment when the experts leaves can cause significant disturbance in the functioning the knowledge based system. On a larger socio-economic scale so-called ‘brain-drain’ is well recognised problem affecting entire geo-political regions.

 

Within the institutional fences, the team work and complemental modes of collaboration are officially strongly promoted with a growing emphasis. Yet in the practice, the achieved synergy is too often below the claimed, not to mention the desired levels. Usual approach is to train the individual members, groups and leaders for ‘team skills’ and variety of factors including ‘emotional intelligence’ and ‘cultural tolerance’ are examined.

 

However the declarative commitments, emotional intelligence and cultural tolerance are still only the superficial remedies when compared to addressing the core issues such as the underlying beliefs and the resulting intentions.

 

One of the most detrimental grounds that nests many roots of inefficiency in manned systems is a belief that knowledge should be kept confidential and ‘intellectually protected’.

 

A diametrically opposite belief, namely that the knowledge is a basic agent affecting the chances of survival is one of the principal factors affecting the success of manned system. For example understanding that our fate does not depend on the outcome of competition with other manned systems as much as it will be affected by the outcome of competition with much wider spectrum of processes in our ambient (such as for example global climate change), will affect our beliefs and trigger quite momentous motives which in turn can dramatically change our intentions.

 

Another fundamentally constructive belief is that the overall efficiency, growth and verifiability of knowledge increase dramatically with the sheer quantity of informed participants.

 

Furthermore, a belief that the open and rapid sharing of existing knowledge across the trans-disciplinary boundaries incubates nucleation of new ideas, increases the probability of making viable hypotheses and speeds up the testing procedures, leads to significantly better utilisation of resources and to more rapid progress.

Manned systems cannot successfully compete with each other by hiding or ignoring knowledge. The constructive competition is possible only by means such as

- investing resources and energy to add the quality by using the inter-disciplinary knowledge,

- by publishing new knowledge or providing improved evidence for (validation of) the existing knowledge. (For example, commercial distribution of sustainable products is a significant contribution to evidence of knowledge).

 

Use of jargon and nomenclature that contravene to the concepts defined by other disciplinas are symptoms of lack of inter-disciplinary communication due to the absence of above listed beliefs. Direct consequences such as the motive mismatch and divergence of intentions lead to further deterioration in communication and lower the efficacy of application of relevant knowledge.

 

Construction of scientific knowledge can be seen as a struggle over who should define the terms and conditions of legitimate fields of research. It has to do with who sets the discourse and with “fashions” as well. [58]

 

Basically, the above problems can be seen as an absence of a belief and intention that the knowledge in one disciplinae will be and should be used in all other disciplinas.

World class systems such as Hewlett-Packard Corporation, Sun Microsystems and IBM promote knowledge sharing enhanced by computerised access for all company members. This occurred somewhat spontaneously due to the fact that traditional model of the workplace has changed. Employees work from home, field sites and customer locations. Most recently the number of ‘virtual’ workers has grown exponentially due to the ubiquitous connectivity the Internet.

 

In a 2008 study conducted by IBM, the ability to collaborate effectively across an organisation and to locate experts are cited as critical. International Business Machines Corporation (IBM) has experienced this phenomenon on a massive scale and the major remediating strategy emerged in fact from pursuing the corporation’s core mission: to enable the efficient communication of information.

 

“Smaller” scale companies follow the trend of open sources at a large scale. Companies such as McNeel North America (http://www.rhino3d.com), Wolfram Research (http://www.wolfram.com) and Material Co. (http://www.material.be/), offer various versions of relevant software for free, in anticipation of attracting the interest in services at a higher level of knowledge transfer.

 

It appears that the leaders in academe and other domains of science follow this trend: so-called open sources (free access to knowledge repositories available on the internet) are being developed at an increasing rate. [48]

 

Idea behind the open sources is that this is the best advertisement, and it reflects the reality that the academic institutions cannot commercialise already existing formulations of knowledge. In this domain, the marketable relations include only

- solutions to specific problems, where such solution is needed and required by the society;

- sustainable assets that can be used for testing new hypotheses;

- diplomas and other verifications awarded to students who satisfy relevant requirements.

 

A quite spectacular aftermath of the above revolution in open (free) publishing knowledge on the internet is the rapid vanishing of barriers that obstructed communication amongst the academics and between the academic institutions. Most successful individuals, groups and institutions have made their knowledge instantaneously accessible by means of the internet; those who’s information is not easily accessible lag behind.

 

The strategy of open sources is proposed as the catalyst for breaking through the barriers that impede communication and knowledge transfer within and between teams in industrial productive organisations.

 

It is important to realise that the nowadays artificial intelligence aids facilitate the use of broad variety of audiovisual modes: authors and publishers are not any more limited to the use of alphanumeric (scriptural) modes. This is not to say that the pictorial information should not be supported by text. However, the brevity of inserted text emphasises furthermore the importance of consistent nomenclature.

 

Agile interaction between the artificial intelligence aids (‘computers’, internet and other info-processing aids) and manned systems releases entire spectrum of novel modes of communication and utilisation of knowledge.

 

The model of open sources can be efficiently scaled down and applied within the structure of any manned system. Team reliability and efficiency are directly proportional to the capability to share and activate knowledge stored within the formally structured subsystems. It appears that the barriers such as information divergence, intent disparity, knowledge imbalance and conflict of beliefs can be mitigated by means of proactive use of artificial intelligence aids (computers, internet and other software).

 

Information systems are omnipresent in all human endeavours and today the man-computer interaction lies at the crossroads of many disciplinas. In this new ambient the most attractive publications reflect informed beliefs and constructive intentions thus increasing the probability of efficient growth, sharing and application of knowledge. Knowledge sharing however must not be confused with sharing the decision-making roles and with the responsibility sharing hierarchy.

 

Online availability of knowledge resources, instantaneous communication and updated evidence of validity have become the realm within the leading manned systems of the 21st century.

 

4. Conclusions

 

The de-mystification of science remains incomplete [57]. In this age of knowledge, spectacularly enhanced by artificial intelligence aids, both communication speed and misinformation waste multiply at critical rates. Particularly obstructive is the increase in information entropy as a result of accumulation of homonyms and synonyms combined with other causes of ambiguity. The root causes for the above problems include differences in interpretations of basic epistemological and ontological concepts.

 

It is hypothesised that the whole spectrum of ambiguities and knowledge ownership barriers are symptoms of the underlying issues such as the motive mismatch, intent disparity and discord in beliefs.

 

Traditional scientific methodology emphasised focus on principal factors to explain the observed phenomena. Efforts to rationalise research and compromises to real-time limitations to accessing the sources of global knowledge lead to appearance of jargon, homonymy and synonymy. Important function of concepts represented by homonymic or otherwise ambiguous terms (or even acronyms) is undermined due to the absence of intention to, and/or belief in the possibility (need) for global sharing of new created knowledge. This is in particular damaging for building educational repositories compatible with the global state of the art in science, and certainly hinders cross disciplinary transfer of knowledge.

 

The need to increase the transparency in scientific and engineering nomenclature is vital. The comprehensive review of basic scientific concepts is a task that demands the gathering together of appropriate institutions. Missions of the academe include sustaining the knowledge accessibility. Universities are institutions that carry the responsibility for initiating relevant inter-disciplinary projects aiming at improvement in knowledge disambiguation and more efficient sharing.

 

The treasure of existing languages is already providing us with a variety that can be used to eliminate ambiguities such as homonymy and synonymy. Moreover, new terms can be conveniently coined where needed. In addition, definitions, the building blocks of knowledge, must not be restricted by the old-age perceptions of scriptural writings on the paper; they must reflect the purpose (minimum intent), and must not disregard conformance with correlated concepts. Ideally a link to the sempiternal ambient should be incorporated. Appropriate definitions are probability intensifiers.

 

The perspective of interaction between the artificial intelligence aids and manned systems, and the rise of the open networks of cross-disciplinary knowledge, uncover new gates and dimensions for communication and application of intellectual treasures at the unprecedented rates. However the actual purpose of knowledge treasures should not be lost out of the sight; more attention to disaccord in beliefs and intentions is needed to take better advantages of available knowledge. Sustainability of  life forms is proportional to the efficiency of knowledge sharing and it is encouraging to realize that knowledge is infinitely shareable.

 

Remark: Authors are listed in alphabetical order. Corresponding author is Sead Spuzic (sead.spuzic@unisa.edu.au).

 

5. References

 

[1] P. Gottschalk (2007) “Knowledge Management Systems: Value Shop Creation”, Idea Group Publishing, 2007

[2] Robert L. Goldstone, Yvonne Lippa, Richard M. Shiffrin (2001) “Altering object representations through category learning”, Cognition, 78 (2001) 27- 43

[3] “Webster's Online Dictionary with Multilingual Thesaurus Translation”

http://www.websters-online-dictionary.org (accessed 5th April 2008)

[4] http://www.infosci.cornell.edu/about/index.html (accessed 6th April 2008)

[5] Christopher A. Welty, Jessica Jenkins (1999) "Formal ontology for subject", Data & Knowledge Engineering, Volume 31, Issue 2, September 1999, Pages 155-181

[6] K. R. Livingston, J. K. Andrews, & S. Harnad (1998) “Categorical perception effects induced by category learning” Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, pp 732-753

[7] Madhu C. Reddy and Patricia Ruma Spence (2008) “Collaborative information seeking: A field study of a multidisciplinary patient care team”, Information Processing & Management, Volume 44, Issue 1, January 2008, Pages 242-255

[8] S. Spuzic, K. Abhary, C. Stevens, N. Fabris, J. Rice and F. Nouwens (2005) “Contribution to Cross-disciplinary Lexicon” (Editors: D Radcliffe and J Humphries) Proceedings 4th ASEE/AaeE Global Colloquium on Engineering Education, Sydney, 26-29 September 2005
[9] S. Spuzic and F. Nouwens (2004) "A Contribution to Defining the Term ‘Definition’", Issues in Informing Science and Information Technology Education, Volume 1 (2004) p. 645, 2004

[10] S. Spuzic S, K. Xing and K. Abhary (2008) "Some Examples of Ambiguities in Cross-disciplinary Terminology", The International Journal of Technology, Knowledge and Society, Volume 4, Issue 2, pp.19-28; http://ijt.cgpublisher.com

[11] T. R. Gruber (1993) “A translation approach to portable ontologies”, Knowledge Acquisition, 5(2):199-220, 1993.

[12] G. A. Miller (2001) "Ambiguous Words" (originally published March 2001 at Impacts Magazine) http://www.kurzweilai.net/meme/frame.html?main=/articles/art0186.html (accessed on 13 October 2005)
[13] Jacob Köhler, Stephan Philippi, Michael Specht, Alexander Rüegg (2006) “Ontology based text indexing and querying for the semantic web”, Knowledge-Based Systems, Volume 19, Issue 8, December 2006, Pages 744-754

[14] S. McCarty (2005) "Cultural, Disciplinary and Temporal Contexts of e-Learning and English as a Foreign Language", eLearn MAGAZINE published by ACM-Association for Computing Machinery; http://www.elearnmag.org/subpage.cfm?section=research&article=4-1 (accessed on 23 Sept 2005)
[15] G. L. Downey, J. C. Lucena, B. Moskal, T. Bigley, C. Hays, B. Jesiek, L. Kelly, J. Lehr, J. Miller and A. Nichols-Belo (2005) "Engineering Cultures: Expanding the Engineering Method for Global Problem Solvers" Proceedings 4th ASEE/AaeE Global Colloquium on Engineering Education, Sydney, 26-29 September 2005

[16]  WordNet lexical database for the English language, Cognitive Science Laboratory at Princeton University, http://wordnet.princeton.edu/  (accessed on 5th November 2006)

[17] http://www.medicineword.com/ (accessed on 15th April 2008)

[18] D. W. Smith (2003) 'Phenomenology', Stanford Encyclopedia of Philosophy, http://plato.stanford.edu/entries/phenomenology/ accessed 15 April 2008

[19] B. Berke (1978) “A generative view of mimemis”, Poetics, Vol 7, Issue 1, March 1978, Pages 45-61

[20] S. Brinkkemper (1996) “Method engineering: engineering of information systems development methods and tools”, Inform. Software Technol. 38 (1996), pp. 275–280.

[21] Zi-Xing Cai (1997) “Intelligent Control: Principles, Techniques and Applications” (Series in Intelligent Control and Intelligent Automation - Vol. 7) publisher: World Scientific. 1997

[22] S. Estrada-Flores, D. J. Cleland, A. C. Cleland, R. W. James (2003) “Simulation of transient behaviour in refrigeration plant pressure vessels: mathematical models and experimental validation” International Journal of Refrigeration, Volume 26, Issue 2, March 2003, Pages 170-179

[23] D. E. Flage (2006) “George Berkeley”, the Internet Encyclopedia of Philosophy, (editors J Fieser and B Dowden http://www.iep.utm.edu/; http://www.iep.utm.edu/b/berkeley.htm (accessed 3rd May 2008)

[24] Arthur Smith (2008) “It’s About Time: Reframing the Context of the Mind-body Debate” to be presented as a poster abstract at the 2008 Conference "Towards a Science of Consciousness," Tucson, AZ, USA, April 8 – 11, 2008. (drsmith@noetichealth.com ) http://www.arthursmithphd.com/writings.htm (accessed 3rd May 2008)

[25] David Ray Griffin (1998) “Unsnarling the World-Knot: Consciousness, Freedom, and the Mind-Body Problem” University Of California Press (accessed 13 May 2008) http://content.cdlib.org/xtf/view?docId=ft8c6009k3&brand=ucpress

[26] J. Kim (1995) "Mind-Body Problem", Oxford Companion to Philosophy. Ted Honderich (ed.). Oxford:Oxford University Press. 1995.

[27] Clarence Irving Lewis, 1960 (1918) “A Survey of Symbolic Logic” Dover.

[28] Anna Merzlyak, Seung-Wuk Lee (2006) “Phage as templates for hybrid materials and mediators for nanomaterial synthesis” Current Opinion in Chemical Biology, Volume 10, Issue 3, June 2006, Pages 246-252

[29] Environmental Protection Agency US (http://www.epa.gov/) http://www.epa.gov/OCEPAterms/vterms.html (accessed on 20th May 2008)

[30] Max Black (1934, 2000) “The Nature of Mathematics, A Critical Survey”

Publisher London : K. Paul, Trench, Trubner & Co., Ltd.; New York, Harcourt, Brace, and Company, 1934.

[31] Google Inc. http://www.google.com

[32] Amit Dhir (2004) “The Digital Consumer Technology Handbook” Elsevier Inc.

[33] The UNESCO Thesaurus, http://databases.unesco.org/thesaurus/ (accessed on 13 October 2005)

[34] “UN glossaries UN interpreters’ resource page… “ http://un-interpreters.org/glossaries.html & http://databases.unesco.org/thesaurus/other.html (accessed on 13 October 2005)

[35] “Handbook on geographic information systems and digital mapping” Department of Economic and Social Affairs, Statistics Division, Studies and Mehods, UN Publications, New York, 2000

[36] Hatem Allouche and Marc Monthioux (2005) “Chemical vapor deposition of pyrolytic carbon on carbon nanotubes. Part 2. Texture and structure”, Carbon, Volume 43, Issue 6, May 2005, Pages 1265-1278

[37] A. Biaggi-Labiosa, L.F. Fonseca, O. Resto and I. Balberg (2008)"Tuning the cathodoluminescence of porous silicon films" Journal of Luminescence, Volume 128, Issue 3, March 2008, Pages 321-327

[38] S. Spuzic, J. O'Brien, C. Stevens (2006) "Basics of Manufacture", eLearning textbook on DVD, Pixelwise Pty Ltd 2008.

[39] Company Advanced Materials Associates

http://www.advancedmaterialsassoc.com accessed 28 December 2007

http://www.advancedmaterialsassoc.com/faq-mateng-concepts-6.html

[40] D. B. Richman, D. C. Lightfoot, C. A. Sutherland, And D. J. Ferguson (1993) “A Manual of the Grasshoppers of New MexicoOrthoptera: Acrididae and Romaleidae”, New Mexico State University Cooperative Extension Service, Las Cruces, NM, September 1993 (modified for electronic publication by Spencer Schell, University Of Wyoming, 2003)

http://www.sdvc.uwyo.edu/grasshopper/ghnmglos.htm

[41] S. Hossein Nedjad and A. Farzaneh (2007) "Formation of fine intragranular ferrite in cast plain carbon steel inoculated by titanium oxide nanopowder" Scripta Materialia, Volume 57, Issue 10, November 2007, Pages 937-940

[42]  Wikipedia, Wikimedia Foundation, http://en.wikipedia.org (accessed on 5th November 2006)

[43] N. Markosian (2008) ‘Time’, The Stanford Encyclopedia of Philosophy, http://plato.stanford.edu/entries/time/ accessed 27 December 2007

[44] Jan H. van Bemmel, Erik M. van Mulligen, Barend Mons, Marc van Wijk, Jan A. Kors, Johan van der Lei (2006) ”Databases for knowledge discovery: Examples from biomedicine and health care”, International Journal of Medical Informatics, Volume 75, Issues 3-4, March-April 2006, Pages 257-267

[45] A. Gupta (2008) “Definitions” The Stanford Encyclopedia of Philosophy, http://plato.stanford.edu/entries/definitions/ (accessed 29th May 2008)

[46] Thomas Hofweber (2004) “Logic and Ontology” The Stanford Encyclopedia of Philosophy, http://plato.stanford.edu/entries/logic-ontology/ (accessed 30th May 2008)

[47] T. Gruber “What is an Ontology?” http://www-ksl.stanford.edu/kst/what-is-an-ontology.html (accessed 7 Novemb 2006)

[48] http://www.geocities.com/ontology2008/openSources.html (accessed 2 Jun 2008)

[49] A. Newell and H.A. Simon (1972) “Human Problem Solving”, p. 20, 1972, reprinted by permission of Prentice-Hall Inc., Englewood Cliffs

[50] Andrew Basden, Heinz K. Klein (2008) “New Research Directions for Data and Knowledge Engineering: A Philosophy of Language Approach” Data & Knowledge Engineering, In Press, Accepted Manuscript, Available online 28 May 2008

[51] Chaomei Chen, Il-Yeol Song, Xiaojun Yuan, Jian Zhang (2008) “The Thematic and Citation Landscape of Data and Knowledge Engineering (1985-2007)”Data & Knowledge Engineering, In Press, Accepted Manuscript, Available online 28 May 2008

[52] R. van de Riet (2008) “Twenty-five years of Mokum: For 25 years of data and knowledge engineering: Correctness by design in relation to MDE and correct protocols in cyberspace” Data & Knowledge Engineering, In Press, Uncorrected Proof, Available online 29 April 2008

[53] R. van de Riet (1992) “Preface: Linguistic Instruments in Knowledge Engineering

(LIKE)”. Data & Knowledge Engineering, 8:187-9. (1992).

[54] H. Weigand (1992) “Assessing Functional Grammar for knowledge representation”. Data

& Knowledge Engineering, 8:191-203. (1992).

[55] Gholam Reza Fallahi, Andrew U. Frank, Mohammad Saadi Mesgari, Abbas Rajabifard

(2008) “An ontological structure for semantic interoperability of GIS and environmental modelling”

International Journal of Applied Earth Observation and Geoinformation, In Press, Corrected Proof, Available online 12 March 2008

[56] Ning Huang, ShiHan Diao "Ontology-based enterprise knowledge integration"

Robotics and Computer-Integrated Manufacturing, Volume 24, Issue 4, August 2008, Pages 562-571

[57] A Broers "2005 - Lord Broers: The Triumph of Technology"; BBC (British Broadcasting Corporation); Reith Lectures http://www.bbc.co.uk/radio4/reith2005/ (accessed 19th July 2008)

[58] L M Madsen, H K Adriansen “Knowledge constructions in research communities: The example of agri-rural researchers in Denmark”, Journal of Rural Studies, Volume 22, Issue 4, October 2006, Pages 456-468

[59] J S Madin, S Bowers, M P Schildhauer, M B Jones “Advancing ecological research with ontologies”,

Trends in Ecology & Evolution, Volume 23, Issue 3, March 2008, Pages 159-168

[60] E Domínguez, M A Zapata “Noesis: Towards a situational method engineering technique”

Information Systems, Volume 32, Issue 2, April 2007, Pages 181-222

[61] N Guarino “Formal ontology, conceptual analysis and knowledge representation"

International Journal of Human-Computer Studies, Volume 43, Issues 5-6, November 1995, Pages 625-640

[62] "Definitions of Ontology from Christian Wolff to Edmund Husserl"  in Corazzon R, "Theory and History of Ontology. A Resource Guide for Philosophers" at http://www.formalontology.it/

http://www.formalontology.it/ontology-definitions-one.htm  retrieved 4th August 2009

 

 

APPENDIX: Geometric Topology

 

A convenient introduction to understanding the concept of ‘topology’ is discussion of geometric topology. For ‘geometric topology’ the hypernym is ‘science of geometry’. It can be stated that what distinguishes different kinds of geometry from each other (including topography and structure as meronyms of geometry) is in the kinds of transformations that are allowed before you really consider something changed. In ordinary Euclidean geometry, you can move, rotate and flip something over, and it is still considered to be unhanged, but you can't stretch or bend it. This is called ‘congruence’ in geometry class. For example, three 2-dimensional phenomena are congruent if you can lay one on top of the other in such a way that they exactly match. [30]

 

In projective geometry two geometric phenomena are considered the same if they are both views of the same object. For example, look at a plate on a table from directly above the table, and the plate looks round, like a circle. But walk away and look at it, and it looks like an ellipse, because of the angle you're at. The ellipse and circle are projectively equivalent. [30]

 

In other words: a set of permitted modifications is defined which in geometric topology allows classification of the modified shapes within the same class. For example: any continuous change which can be continuously undone is allowed. So a circle is the same as a triangle or a square, because you just ‘pull on' parts of the circle to make corners and then straighten the sides, to change a circle into a square. And vice versa: you just ‘smooth it out' to turn it back into a circle.  [30]

In a 3-dimensional space, a sphere (e.g. made out of plastic clay) can be squashed into a disk – these two geometric phenomena are topographically equivalent. However the following two 2-dimensional phenomena are not topologically equivalent:

О and 8

 

This is because the middle point in the case of “8” cannot be stretched in a continuous manner within the 2 dimensional space. The shape of the upper or lower or both ‘halves’ in’8’ can be changed in many ways, but the ‘intersecting point’ has to be preserved. 

 

Differing options would appear if ‘8’ would be analysed within the class of knots in a 3-dimensional space. [30]

 

It is important to note instructiveness of the above example: providing that we have defined a limited number of relations (which matter for some reason), we can  consider and compare a variety of options, and treat them as ‘different’ or ‘identical’ without taking in account  insignificant criteria.

 

It is proposed in this present treatise to define topology as the science of homotopy.

 

Make a Free Website with Yola.