This document summarizes a transcript from the PEMT '06 conference discussing challenges with terminology across disciplines and proposes approaches to address ambiguities. It notes how knowledge evolution has led to specialized terminology that may only be understood by experts, hindering cross-disciplinary communication. Defining terms unambiguously is important for knowledge management. The document provides examples of ambiguous terms like homonyms and synonyms and proposes establishing a transparent, inter-disciplinary lexicon using fundamental disciplines like physics and mathematics to prioritize terms. It emphasizes the need to review scientific terminology to remove ambiguity and proposes criteria to clearly define terms.
This presentation aspires to pinpoint the necessity of eliminating homonyms and synonyms. It attempts to illustrate the impact of misinforming that results from lexical disorder within the context of cross-disciplinary transfer of knowledge, standards setting and global business communication. The examples of homonyms and synonyms that have been observed to cause misinterpretations are presented. The genuine need for introducing a multidisciplinary transparent lexicon is advocated. A definition of a term "definition" is presented. Exemplary definitions are provided as models of transparent lexical terms. It is recommended that a hierarchy of terminology be adopted, giving the most fundamental disciplines the priority, and making sure that the other disciplines conform. A properly defined term is an information probability intensifier.
A Natural Logic for Artificial Intelligence, and its Risks and Benefits gerogepatton
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
Classical logic has a serious limitation in that it cannot cope with the issues of vagueness and uncertainty
into which fall most modes of human reasoning. In order to provide a foundation for human knowledge
representation and reasoning in the presence of vagueness, imprecision, and uncertainty, fuzzy logic
should have the ability to deal with linguistic hedges, which play a very important role in the modification
of fuzzy predicates. In this paper, we extend fuzzy logic in narrow sense with graded syntax, introduced by
Nova´k et al., with many hedge connectives. In one case, each hedge does not have any dual one. In the
other case, each hedge can have its own dual one. The resulting logics are shown to also have the Pavelkastyle
completeness.
Lecture 2: From Semantics To Semantic-Oriented ApplicationsMarina Santini
From the "Natural Language Processing" LinkedIn group:
John Kontos, Professor of Artificial Intelligence
I wonder whether translating into formal logic is nothing more than transliteration which simply isolates the part of the text that can be reasoned upon using the simple inference mechanism of formal logic. The real problem I think lies with the part of text that CANNOT be translated one the one hand and the one that changes its meaning due to civilization advances. My own proposal is to leave NL text alone and try building inference mechanisms for the UNTRANSLATED text depending on the task requirements.
All the best
John"
This presentation aspires to pinpoint the necessity of eliminating homonyms and synonyms. It attempts to illustrate the impact of misinforming that results from lexical disorder within the context of cross-disciplinary transfer of knowledge, standards setting and global business communication. The examples of homonyms and synonyms that have been observed to cause misinterpretations are presented. The genuine need for introducing a multidisciplinary transparent lexicon is advocated. A definition of a term "definition" is presented. Exemplary definitions are provided as models of transparent lexical terms. It is recommended that a hierarchy of terminology be adopted, giving the most fundamental disciplines the priority, and making sure that the other disciplines conform. A properly defined term is an information probability intensifier.
A Natural Logic for Artificial Intelligence, and its Risks and Benefits gerogepatton
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
Classical logic has a serious limitation in that it cannot cope with the issues of vagueness and uncertainty
into which fall most modes of human reasoning. In order to provide a foundation for human knowledge
representation and reasoning in the presence of vagueness, imprecision, and uncertainty, fuzzy logic
should have the ability to deal with linguistic hedges, which play a very important role in the modification
of fuzzy predicates. In this paper, we extend fuzzy logic in narrow sense with graded syntax, introduced by
Nova´k et al., with many hedge connectives. In one case, each hedge does not have any dual one. In the
other case, each hedge can have its own dual one. The resulting logics are shown to also have the Pavelkastyle
completeness.
Lecture 2: From Semantics To Semantic-Oriented ApplicationsMarina Santini
From the "Natural Language Processing" LinkedIn group:
John Kontos, Professor of Artificial Intelligence
I wonder whether translating into formal logic is nothing more than transliteration which simply isolates the part of the text that can be reasoned upon using the simple inference mechanism of formal logic. The real problem I think lies with the part of text that CANNOT be translated one the one hand and the one that changes its meaning due to civilization advances. My own proposal is to leave NL text alone and try building inference mechanisms for the UNTRANSLATED text depending on the task requirements.
All the best
John"
Semantic Rules Representation in Controlled Natural Language in FluentEditorCognitum
Abstract. The purpose of this paper is to present a way of representation of semantic rules (SWRL) in controlled natural language (English) in order to facilitate understanding the rules by humans interacting with a machine. The rule representation is implemented in FluentEditor – ontology editor with controlled natural language (CNL). The representation can be used in a lot of domains where people interact with machines and use specialized interfaces to define knowledge in a system (semantic knowledge base), e.g. representing medical knowledge and guidelines, procedures in crisis management or in management of any coordination processes. Such knowledge bases are able to support decision making in any discipline provided there is a knowledge stored in a proper semantic way.
Conceptual Interoperability and Biomedical DataJim McCusker
The goals of conceptual interoperability are:
Make similar but distinct data resources available for search, conversion, and inter-mapping in a way that mirrors human understanding of the data being searched.
Make data resources that use cross-cutting models (HL7-RIM, provenance models, etc.) interoperable with domain-specific models without explicit mappings between them.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matterPeter Jones
A presentation (at short notice) on
THRESHOLD CONCEPTS
- Educational research
CONCEPTUAL SPACES
- Cognitive Sciences and Linguistics
HODGES' HEALTH CAREER - CARE DOMAINS - MODEL
- Nursing, Healthcare Education
With discussion relating to programming and Drupal.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
In this paper we present the SMalL Ontology for malicious software classification, SMalL Java Application for antivirus systems comparison and the SMalL knowledge based file format for malware related attacks. We believe that our ontology is able to aid the development of malware prevention software by offering a common knowledge base and a clear classification of the existing malicious software. The application is a prototype regarding how this ontology might be used in conjunction with known antivirus capabilities to offer a comprehensive comparison.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijwscjournal
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijasuc
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
Semantic Rules Representation in Controlled Natural Language in FluentEditorCognitum
Abstract. The purpose of this paper is to present a way of representation of semantic rules (SWRL) in controlled natural language (English) in order to facilitate understanding the rules by humans interacting with a machine. The rule representation is implemented in FluentEditor – ontology editor with controlled natural language (CNL). The representation can be used in a lot of domains where people interact with machines and use specialized interfaces to define knowledge in a system (semantic knowledge base), e.g. representing medical knowledge and guidelines, procedures in crisis management or in management of any coordination processes. Such knowledge bases are able to support decision making in any discipline provided there is a knowledge stored in a proper semantic way.
Conceptual Interoperability and Biomedical DataJim McCusker
The goals of conceptual interoperability are:
Make similar but distinct data resources available for search, conversion, and inter-mapping in a way that mirrors human understanding of the data being searched.
Make data resources that use cross-cutting models (HL7-RIM, provenance models, etc.) interoperable with domain-specific models without explicit mappings between them.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matterPeter Jones
A presentation (at short notice) on
THRESHOLD CONCEPTS
- Educational research
CONCEPTUAL SPACES
- Cognitive Sciences and Linguistics
HODGES' HEALTH CAREER - CARE DOMAINS - MODEL
- Nursing, Healthcare Education
With discussion relating to programming and Drupal.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
In this paper we present the SMalL Ontology for malicious software classification, SMalL Java Application for antivirus systems comparison and the SMalL knowledge based file format for malware related attacks. We believe that our ontology is able to aid the development of malware prevention software by offering a common knowledge base and a clear classification of the existing malicious software. The application is a prototype regarding how this ontology might be used in conjunction with known antivirus capabilities to offer a comprehensive comparison.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijwscjournal
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijasuc
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
Marcelo Funes-Gallanzi - Simplish - Computational intelligence unconferenceDaniel Lewis
At the computational intelligence unconference 2014, Marcelo Funes-Gallanzi presented Simplish, a system for the conversion of text into Simple English. Here are his slides.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Advertising Sci-Fi novel The 4th Birth.pdfSead Spuzic
This is a promotional pamphlet for a Sci-Fi novel that's enhanced with hyperlinks leading to validated scientific information. It's crafted to inspire young students—and anyone thirsting for knowledge—to learn by following their curiosity. The core aim of the storytelling is to enlighten and educate readers on significant subjects. The topics covered span a broad spectrum, reaching as far as the cutting-edge advancements in futurology.
This inspiring novel is grounded in the concept of education driven by the curiosity of learners. This science-fiction narrative incorporates hyperlinks that guide readers towards a variety of credible scientific and educational resources. This feature affords a level of freedom and choice that traditional print novels simply cannot offer.
The first story The 4th Birth is (just seemingly) about the Lemurians, an ancient race which appears to have existed prior to and during the time of the equally mysterious empire of Atlantis. Some authors believe that Lemurians developed their civilisation (also called Lapita and Mu - Motherland) some 70,000 to 80,000 years ago, mainly in the South-West Pacific, between China and Australia.
Lemurians were living through alternating periods of peace and prosperity, conflicts and crises over the millennia. During this time, they made considerable advances in culture, politics, sciences and technology causing only minor ecological catastrophes. At the peak of their civilisation, the Lemurian people were both technically advanced and very spiritual. However, they were unaware that the indifferent Nature was leading their world towards an ultimate cataclysm. Fortunately, alongside the Lemurians and several neighbouring nations that worked hard to enslave one another, another civilisation (if one is to believe the fragments that appear in certain legends), much older and hence somewhat more mature, was witnessing this course of events.
The Visitors who belonged with an entirely different phylogeny, the highly developed race of some system of evolution from the infinitely distant past, became aware of signs of the rarest phenomenon in the Universe - Intelligence. During a period of several centuries, the involvement of Visitors was in the role of invisible observers. They did not intervene, or become involved with subjects of their study. But when the course of Planet Earth turned towards the catastrophe, they decided to step in, and help save at least some members of the human race.
The Visitors who belonged with an entirely different phylogeny, the highly developed race of some system of evolution from the infinitely distant past, became aware of signs of the rarest phenomenon in the Universe - Intelligence. During a period of several centuries, the involvement of Visitors was in the role of invisible observers. But when the course of Planet Earth turned towards the catastrophe, they decided to step in, and help save at least some members of the Lemurian race.
Engineering Design is an iterative decision-making process used to devise a component, product, process, or system to meet the needs and functions desired by the user in a sustainable manner.
Engineering Design is a decision making process (often iterative or recursive) in which the sciences are applied to modify/create something to meet predefined objectives (specifications). Basic stages of the design process include establishment of objectives and criteria, analysis, synthesis, definition of actual manufacturing techniques and routes as well as the modes of usage, maintenance and disposal.
Following the concerns prompted by the lack of technological expertise, it is proposed that education be further enhanced by promoting entrepreneurial links between Manufacturing and Academe. Students should be fully employed in real manufacturing systems over an extended period of their study. There should be no dilution of academic disciplines; however, university education should be counterbalanced by direct industrial experience.
A statistical approach to defect detection and discrimination has been applied to the case of hot rolled steel. The
probability distribution of pixel intensities has been estimated from a small set of images without defects, and
this distribution is used to select pixels with unlikely values as candidates for defects. Discrimination of true
defects from random noise pixels is achieved by a dynamical thresholding procedure, which tracks the behaviour
of clusters of selected pixels for varying threshold level.
Processes based on fluidity and solidification, or simply “casting”, include manufacturing techniques whereby molten material is poured or forced into a mould and allowed to harden. Appropriate variants of this technique are particularly suitable for the economical production of complex shapes, ranging from mass-produced parts for the automotive industry to one-of-a-kind production of jewellery or massive machinery.
Presented at the World Conference on Educational Sciences http://www.wces2009.org/
February 04-07, Nicosia, Cyprus
Abstract: http://spuzic.synthasite.com/knowledge_-basics.php
Live presentation (Youtube): http://au.youtube.com/watch?v=ZYwYGXuVhqo
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
1. Contribution to Knowledge Management:
Cross-disciplinary Terminology
PEMT ’06 transcript
Sead Spužić a), Kazem Abhary a), Clement Stevens b) and Faik Uzunović c)
a) b)
University of South Australia KFUPM University c) University of Zenica
Key words: Disambiguation, definition, homonymy, informatics, knowledge, lexicon,
management, multidisciplinary, terminology, thesaurus, synonymy
Abstract:
The evolution of knowledge has imposed branching into disciplines that use terms understood
“correctly” only by experts. Globalisation favours cross-disciplinary and transparent
communication. However, these trends have uncovered impedances such as prolixity, ambiguity and
jargon. Internet enables communication with the speed of light, thus exposing other limits to
knowledge transfer, such as misinformation and misunderstanding. Knowledge is transferred by
interaction of language with other models (e.g. figures) and by demonstrations. Transparency of
terminology is critical for knowledge management. This treatise presents an axiomatic definition of
the term 'definition' itself, in order to enable a rational analysis of lexical elements - the words. The
examples of confusing terms (homonyms, synonyms) are discussed. A trend of using inter-
disciplinary and transparent lexicon is proposed, with adopting a hierarchy of terms that allocates
the priority to fundamental disciplines (mathematics, physics). Scientific lexicon should attribute to
each definition a unique set of words. The need to expunge scientific and technical language of
ambiguity is urgent and the comprehensive review and “cleaning” of scientific terms is a task that
demands the gathering together of appropriate institutions. The key to disambiguation of scientific
language is in defining quantifiable criteria.
Intro
In this eternal and infinite ambient, our fate depends on our knowledge. Although Man is the most
developed amongst the phenomena we know, this does not guarantees our survival. Rather than
indulging in this relative perception, it is reasonable to assume that our ambient - the Universe - may
soon bring in new challenges that stretch beyond our current capabilities. A rational strategy is to
speed up both the development and transport of our knowledge. This can be enhanced by sharing
and disseminating knowledge to adequate human resources, not to mention broader possibilities.
Academe and other structures concerned with broadening and disseminating knowledge are indeed
conscious of the need for globalization and disambiguation of this invaluable treasure. Reference
publications [1-20] used in this transcript present only a very limited and arbitrary selection amongst
numerous sources which present evidence of this awareness for example, by the virtue of motions
such as European University Association [2], The Bologna Process [3] or UNESCO Thesaurus
[13,14].
1
2. One of the principal media for transport of knowledge is a language; its basic elements are the words
(terms). Lexicon (alphabetically arranged lists of words setting forth their meanings and etymology)
is direct hyponym of the term ‘knowledge’ [12].
The category of so-called "closed class words" has a fixed, limited number of words which
themselves have permanent, final form and meaning; new words are rarely added. The members
include: pronouns, prepositions, determiners and conjunctions [10].
In the case of so-called "open class words", containing nouns, verbs, adjectives and adverbs, new
words can be added as they become necessary [10]. However, much too frequently, new words are
added although old words, providing a satisfactory meaning, do already exist. This causes
synonymy (e.g. "open class words" are also called "lexical words", while "closed class words" are
also termed “structural” or “function” or “grammatical” words). Or, vice versa, new meanings are
attached to words used in another discipline to denote differing concept, thus causing homonymy
(e.g. word 'discipline' could mean a branch of knowledge; "in what discipline is his doctorate?";
"anthropology is the discipline focused on study of human beings"; but the same word could mean
‘punishment’ o rat least ‘orderly prescribed conduct or pattern of behaviour’).
“There does not seem to be a consensus about what many of the basic terms mean, or which is the
overarching concept, … under which other terms might be presumed to be subsets. …. (C)learly, the
multiplicity of definitions for the same concepts, false synonyms and so forth show that the world of
scholarship needs an approach to definitions of sufficient dimensionality.” [4]
“The recent globalisation trends show that, on all fronts - education, marketing, industry, science,
social standard infrastructure, health - we need a common, well defined, language. Workers in all
disciplines are expected to function effectively in global trans-disciplinary communities.”[5]
“The comprehensive review and ‘cleaning’ of scientific terminology is certainly an immense task
that demands the gathering together of competent institutions. The need to expunge scientific and
technical language of ambiguity and prolixity is urgent and becoming increasingly so.” [8]
Rational analysis of lexical elements - the words - and their relations requires at least an axiomatic
definition of the term 'definition' itself. Hence an initial definition is proposed herewith, in the
following section. In addition, a number of examples of ambiguous terms is presented along with an
attempt to propose their disambiguation. In doing so, an effort is made to avoid homonymy,
synonymy, circularity and other ambiguities. The presented examples are rather arbitrarily chosen
illustrative cases of ambiguous terms and proposed clarifications; an attempt is made to lay down
the initial formulations thus opening the floor for further documented improvements.
Theoretical concepts are presented in references [9] and [20]; for convenience, some key definitions
are cited in full extent.
Definition (excerpt cited from [20])
Minimum Intent: The following definition of a term 'definition' is presented as a reference, (a metric,
a comparator, a norm) that must not be violated when defining scientific and engineering terms.
Axioms:
1) ‘Something’ is a term that has a most general meaning, it can mean anything (but it does not
automatically include ‘everything’).
2) 'Ambient' is everything in the vicinity of, and, to a certain degree, within something.
3) ‘Event’ is something that can be distinguished from ambient.
4) ‘Relation’ is something involving, at least, two events.
2
3. 5) ‘System’ is constituted by at least two relations; this implies that a system also includes, at least,
two events.
6) ‘Phenomenon’ is a generic term (hypernym) for the above terms, providing that one or several of
human senses indicate (directly or indirectly) existence of so termed system, relation, event,
ambient, or something else.
7) All other terms used within this theorem - apart from the term “definition” and the terms listed in
inverted commas under 1) to 6) above - are already intrinsically known; understanding of each of
these terms does not contradict to any other term, and it does not violate logics. Note: most of these
terms will be defined once the definition of the term ‘definition’ is agreed upon; some proposed
definitions are given in this discourse.
Theorem:
"Definition" is a fixed, static form (a model; a concept; an appearance of something as distinguished
from the substance of which it is made; something autonomous from its own representation, imprint,
or description) of some relation(s) that significantly increases the probability of realisation of an
intended (premeditated) change of some phenomenon (or phenomena). Such a change is to be
achieved by an entity that is capable of utilising this definition for such a specified purpose. A
definition cannot be generated, or used without the existence of a system, which is organised and
structured above certain level of chaos. However, once it is generated and recorded, a definition can
continue to exist (to be recorded) without the existence of the mentioned entity. A definition should
be complemented with a minimum intent statement: a context that delimits a minimum domain of
purposes for which it can be used. This statement does not exclude the possibility of using the same
definition correctly for some other purpose. However, this extended use must not violate (contradict)
already established meaning; e.g. this must not cause synonymy or homonymy.
A definition must be complemented with axioms, with one or more examples, and, when needed and
possible, with figures and animated representations.
Definitions are necessary bits needed to construct and communicate the subject of knowledge. A
definition is built by means of its structural components: pieces of information. Information is built
by virtue of its construction elements (signals of various kinds); the most frequently used include
figures and terms. Terms include symbols, numbers and words, and although they can be transferred
by means of figures, they can also be transferred by means of sounds which are registered by
hearing senses. It is worth noting that information media can be mutually translated, i.e. visual info
can be translated into information received by tactile or hearing senses. History of media used to
record an information and a definition shows a variety of options. Alphabetic writing (in which
consonant and vowel sounds are presented by letters or other symbols such as Braille characters and
Morse codes) is the most widespread system, but it is not the earliest, nor is it the only one. Writing
has evolved from an extension of pictures that iconically represented some thing or action and then
the word that bore that meaning. This approach led to so-called character script, such as that of
Chinese, in which each word is represented by a separate symbol.
There is no reason for restricting definition to alphanumeric records only; indeed, the figures
(including drawings) are very efficient in carrying comprehensive information. Many sciences have
accepted ideograms to convey sophisticated notions. For example, in mathematics, symbols π, ∞,
‰, ∫, ≥, represent erudite concepts. Optimal solution is a combination of text and figures (an
animation and sound may be added, when necessary).
3
4. Aspects of information metrics were discussed by C E Shannon [1] who furthered the principles of
information theory and endowed the word information with a measure, so-called info-entropy:
..................................... (1)
H = info-entropy (the expected value of self-information),
p(i) = probability of understanding i-th interpretation of the presented term
n = number of possible interpretations of the presented term.
"The choice of a logarithmic base corresponds to the choice of a unit for information measure. If the
base 2 is used the resulting units may be called binary digits, or more briefly bits. A device with two
stable positions, such as a relay or a flip-flop circuit, can store one bit of information. N such
devices can store N bits ..." [1]. Using Napier's logarithm in Eq (1) appears more logical; however
transformation from base e = 2.7183... to base 2, is a simple matter of introducing an appropriate
constant ln2 = 0.6931.
A ‘measure’ is a phenomenon used to enable a comparison of (the groups of) other phenomena.
‘Comparison’ is a definition indicating whether (or to what degree) one phenomenon differs from
other phenomena. When comparison indicates that phenomena are sufficiently identical, phenomena
can be counted using numbers. A ‘number’ is a generic measure.
Examples of definitions:
Readily available sources (e.g. dictionaries) define the term “figure” in various ways: (a) a number
symbol, (b) numeral, (c) digit, (d) a geometric form (e.g. a line, triangle, or sphere) especially when
considered as a set of geometric elements (e.g. points) in space of a given number of dimensions, (e)
a diagram or pictorial illustration of textual matter, (f) a short coherent group of notes (sounds) that
may constitute a part of a melody.[8,9]
The first two above definitions, (a) and (b), can themselves be taken as synonymic. The terms
"figure" and "numeral" are synonyms, because both are defined in the same way as follows: "figure"
("numeral") is a conventional symbol (a figure or character) used to represent a number. The
definitions given in (c), (d), (e) and (f) above, have different meanings. Thus the term "figure",
attributed to each of these four cases appears to be a homonym.
By ignoring presence of any noise and assuming 4 equally likely homonyms, according to Eq (1)
information entropy of term ‘figure’ is calculated to be equal to 2 bits.
(Minimum intent:) The following definitions are presented to provide examples how synonyms,
homonyms and other ambiguities can be avoided:
“Figure”: (n) an arrangement of points made within two-dimensional space to present a visual static
impression (a perception) of something (e.g., a figure printed on a book page, showing a front view
of a home).
Still assuming the absence of any noise, after eliminating one of four homonymic ‘figure’ terms, its
information entropy drops 20%, (falls to 1.585 bits). If we eliminate all homonyms, information
entropy drops to zero, and term “figure” becomes self-explanatory, in the absence of another noise.
The following definitions are presented for the sake of making the term 'figure' more distinguishable
from some of its homonyms:
4
5. “Numeral” is any of the elements that can be combined to form numbers in a number system (e.g.,
decimal system, binary system, hexadecimal system, etc.). “Digit” is a figure representing a
numeral; examples: "0", "1", "A". "Number" is a most general measure, systematically ordered and
analysed within mathematics. "Cypher" is a figure representing a number; examples: "10001001",
"10,001.001".
Important capacity of terms - to represent complex information, definition and even the complete
theory - can be seriously hindered by ambiguity, homonymy and synonymy. Thus for example, by
saying that a system is adiabatic, a number of physiochemical relations is ascribed to this system,
assuming that the receiver of this information knows the meaning of term "adiabatic system", i.e.
assuming availability of a disambiguated definition of this term.
Tendency of professional institutions focused on particular filed (e.g. information technology), to
use certain lexical phrases more frequently should not lead to usurpation of a single term, segregated
and disconnected from the original phrase. Local use of an abbreviation is a better solution in such a
case. In distinguished cases, introducing a new term is an appropriate solution; in such events it is
recommended to consult academe before offering such new term to public scrutiny.
'Knowledge' is a system of 'disciplines' and their relations. A 'discipline' is a system of 'theories',
boundary 'hypotheses' and their relations. 'Theory' is a system of 'definitions'. 'Hypothesis' is a
system of assumptions that may, or may not become definitions. If proven to be fallacies, the
relevant hypotheses should be rejected from the parent discipline, and replaced by other boundary
hypotheses. Rationale for the need for inducting boundary hypotheses at the edge of verified
knowledge can be elucidated using the analogy with the justification for introducing additional
‘uncertain’ digit during recording the measurements by means of significant digits. Any boundary
becomes more vague the closer we approach it. Inasmuch the language presents, with its essentially
mathematical crust, elite medium of knowledge, at its best it will reflect these shades of vague
boundaries. However, we must act in the direction of overcoming, not artificially enlarging these
ambiguities.
Reference database
The scientific community and broader society are well aware of problems caused by inconsistencies
in defining the key elements in English language – words [4-19]. Accordingly, a number of projects
has been launched aiming at contributing to disambiguation of the English terms; examples include
The American Heritage Book of English Usage, WordNet and UNESCO Thesaurus.
The American Heritage Book of English Usage presents current problems in English usage to enable
an informed selection of terms. It suggests answers to questions such as: Has a particular usage
been criticized for substantial reason in the past? What are the linguistic and social issues involved?
Have people frequently applied this usage in the past? This source employs The Usage Panel and
Usage Ballots to collate opinions of the American Heritage Usage Panel (158 members), which has
been in existence since 1964. While the ballots are not scientific surveys in that they are not
conducted under controlled circumstances with stringent questioning criteria, they are nonetheless
carefully worded to get useful responses. The examples discussed by ballots are sentences adapted
from actual citations, presenting a number of cases, giving a specific usage in a variety of different
linguistic environments. Many words have a number of meanings, and experience has shown that
the panel’s opinions about a usage can vary considerably. [11]
5
6. In most ballots the panelists are asked whether they find a particular word or construction to be
acceptable or not in formal standard English. In reality, many shades of acceptability do exist. What
one panelist approves enthusiastically, another may accept only cautiously. A compromise has been
made, deciding that it is not practical to differentiate degrees of approval or disapproval. For certain
controversial usages a question allows for the option of indicating acceptability in informal contexts
and for and indication of preferences or for providing alternative ways of saying something. [11]
The fact that a word has a lengthy history of use by many provides a compelling argument for its
continued use today. But sometimes historical precedent clashes with contemporary attitudes. In
these cases, both sides of the controversy are presented, and the historical precedent given priority
even if it contradicts to the judgments of the majority of panelists. On the other hand, some
expressions have become so stigmatized that even the history may not save them from provoking a
negative response in a good portion of your readers. In these cases, a warning is provided about the
consequences of using a stigmatized usage. [11]
With ballot responses going back to the 1960s, the issue of historical perspective requires to be
addressed. The book offers results from surveys done in 1987 and later, while the results of an
earlier survey are presented whenever it is 'feel it can help in adjudicating an issue'. [11]
Taking in account a “non-scientific” survey may seem odd; however, rationales for maintaining
strong links with “common” English language are manifold:
- Efforts to improve scientific terminology would be hampered without analysing language
heritage, including its fallacies;
- Knowledge dissemination requires presenting its theories and hypotheses using the language
of common sense;
- Our existence without knowledge would be impossible; without the arts it would become
distorted.
WordNet is an online ('electronic') lexical reference system (database) [12]. WordNet was
developed starting with 1985, by the Cognitive Science Laboratory at Princeton University under
the direction of Professor G. A. Miller. This online lexical reference system is designed on the basis
of current psycholinguistic theories of human lexical memory. English nouns, verbs, adjectives and
adverbs are organized into synonym sets (synsets), each representing one underlying lexical
concept. Synsets are used as basic units of semantic meaning and linked to a large collection of
semantic relations including hyponymy and antonymy. In other words, WordNet organizes lexical
information in terms of word meanings, rather than word forms. The purpose is manifold: word
sense identification, information retrieval, selectional preferences of verbs, and lexical chains.
WordNet can be used to produce a combination of dictionary and thesaurus that is more intuitively
usable, and to support automatic text analysis and artificial intelligence applications, such as
machine translation.
Extensive bibliography [17] listing research publications that refer to the WordNet lexical
database, adds evidence to significance of related problems. For example, WordNet is used in
numerous researches aiming at text classification by means of artificial intelligence, based on the
hierarchy of hypernyms, hyponyms, holonyms, meronyms and sister terms. [18, 19]
UNESCO (United Nations Educational Scientific and Cultural Organization) Thesaurus: The
UNESCO Thesaurus is a controlled and structured list of terms used in subject analysis of
6
7. publications in the fields of education, culture, natural sciences, social and human sciences,
communication and information. It covers the major fields of knowledge that constitute the scope of
UNESCO. Continuously updated (it contains 7,000 terms in English, 8,600 terms in French and
6,800 in Spanish); its multidisciplinary ‘terminology’ reflects the evolution of the UNESCO
activities. According to its own definition, 'thesaurus is a controlled and dynamic documentary
language containing semantically and generically related terms, which comprehensively covers a
specific domain of knowledge'. 'Knowledge is information that is presented within a particular
context, yielding insight on application in that context, by members of a community.' This source
further defines 'information' as 'data that has been organized in such a way that it achieves meaning,
in a generalized way'. These definitions are both ambiguous and incomplete. In addition they are
based on undefined terms: ‘meaning’, ‘organized’ and ‘data’. [13, 14]
The UNESCO Thesaurus allows subject terms to be expressed consistently, with increasing
specificity, and in relation to other subjects. It can be used to facilitate subject indexing in libraries,
archives and similar institutions. [13,14]
As in other subject thesauri, the terms in the UNESCO Thesaurus are linked together by three types
of relationships:
(i) Hierarchical relationships, which link terms to other terms expressing more general and
more specific concepts - i.e. broader terms and narrower terms. Hierarchically related terms
are grouped under general subdivisions (known as "microthesauri"), which in turn are
grouped into the areas of knowledge covered by the Thesaurus.
(ii) Associative relationships, which link terms to similar terms (related terms) where the
relationship between the terms is non-hierarchical.
(iii) Equivalence relationships, which link "non-preferred" terms to synonyms or quasi-
synonyms which act as "preferred" terms.
Main sections of the Thesaurus (e.g. Education, Science, Culture) link to the microthesaurus
headings in each section. Each microthesaurus heading links to an alphabetical list of the preferred
terms which are entered under that microthesaurus. Each term includes a link to a display of its
broader terms, narrower terms, related terms, scope notes, non-preferred terms, French equivalent
and Spanish equivalent.
The UNESCO Thesaurus also includes scope notes which explain the meaning and application of
terms, and French and Spanish equivalents of English preferred terms. [13,14]
There is a number of other thesauri under, and beyond the umbrella of United Nations system.
However this reference database did not prevent from appearance of homonymy, synonymy and
ambiguity in educational and scientific publications.
Examples
Several remarks will be useful before presenting examples of synonymy, homonymy and ambiguity.
We address the 'open class words' (also called 'lexical words') within one language only: the
scientific English. Non-coincidental homonymy or synonymy is not discussed in this treatise since it
is assumed that context and syntax attribute the sufficiently clear meaning to this class of words. The
recommended definitions are presented to provide examples of disambiguation; in each case, the
axioms are identical to those presented in section Definition above.
7
8. The presented examples, comments and suggestions are provided for purpose of illustrating the
problems and sketching the possible solutions. Decisions about eliminating homonyms and adopting
definitions can only be made by a broad multidisciplinary academic consortium. Number of
examples is also provided in sources [8, 9, 16 and 20].
i) Terms ‘Terminology’ and ‘Term’
Source WordNet defines ‘terminology’ as a system of words used to name things in a particular
discipline; example: "legal terminology"; "biological terminology". Term ‘nomenclature’ is listed as
a synonym. Direct hypernym is term ‘word’ [21].
Sources [13,14] recognise term ‘terminology’ as a descriptor and suggest its applications in phrases
such as ‘communication terminology’, ‘scientific terminology’, ‘educational terminology’; it lists
usage such as ‘technical terminology. The same sources define term ‘glossary’ as ‘A vocabulary not
necessarily in alphabetic order, with definitions or explanations for all terms.’
It is suggested herewith that term ‘terminology’ be applied in analogy to terms ‘technology’ (science
of techniques), ‘biology’ (science of biosphere), ‘anthropology’ (science of humankind),
‘psychology’ (science of the psyche), etc, hence: ‘Terminology’ (n) is science of terms.
WordNet suggest 8 meanings for term ‘term’. The most differing options are:
a) term (n) is a (special class of) words used for some particular thing, e.g. "he learned many
medical terms"
b) 'a limited period of time'
The following definition of the word ‘term’ is proposed hereby:
‘Term’ (n) is word that denotes something. It is instructive to elaborate on the difference between
the term ‘term’ and its hypernym - ‘word’ . Term ‘word’ denotes all grammatical variations of
nouns, verbs, adjectives, adverbs, pronouns, conjuctions, prepositions and interjections. ‘Term’ is
the lexical model, a concise one-word representation of an event, relation, phenomenon, system,
discipline, theory, or something else. Examples of terms: ‘material’, ‘probability’,’element’.
Example: "'The term preschooler signals another change in our expectations of children. While
toddler refers to physical development, preschooler refers to a social and intellectual activity: going
to school.' Attribution: Lawrence Kutner (20th century), U.S. child psychologist and author.
Toddlers and Preschoolers, introduction (1994). " [15]
ii) Term “Material”
The WordNet [12] provides 5 options. The most differing meanings include:
a) Material (n) is information (data or ideas or observations) that can be used or reworked into a
finished form; "the archives provided rich material for a definitive biography".
b) Material, (n) is the tangible substance that goes into the makeup of a physical object) "coal is a
hard black material"; "wheat is the material they use to make bread".
UNESCO Thesaurus [13,14] provides the following descriptors (suggested as preferred terms):
‘Audiovisual materials’, ‘Building materials’, ‘Composite materials’, ‘Dangerous materials’,
‘Reference materials’, ‘Materials engineering’, ‘Machine readable materials’, ‘Bookform materials’.
8
9. The same source recommends the following descriptor: ‘International circulation of materials’ with
a scope note: 'Use only in relation to agreements that aim to facilitate the international exchange of
materials intended for educational, scientific and cultural purposes.' [13,14]
It appears that “material” is a generic term, sometimes subordinated to hypernyms “matter” and
“substance”. It is recommended herewith to cease this subordination, and introduce the following
generic meaning: Material (n) is physiochemical phenomenon that is detected by various sensors as
solid, liquid or gas. In most cases each of these states can be reversibly transformed into other states
under appropriate combination of temperature and pressure, e.g. water can be cooled to solidify as
ice. The smallest fraction of material are atoms and ions, the largest are celestial objects (such as
Earth or Mars). Beyond these limits, terms such as “subatomic particles”, “electromagnetic field”,
“gamma radiation”, “solar system”, “galaxy” etc are used, none of them to be referred to as
“material”. This is not to say that there are no subatomic particles present within material; also,
current evidence suggests that material exists within the galaxies and other phenomena abroad the
universe. Quantity of material is measured in moles.
Phrases such as “teaching materials” should be understood as “materials prepared for educational
purposes”. For example, graphite powder stored in special beakers for teaching demonstrations in
laboratories, liquid nitrogen kept in special cryogenic storage vessels (dewars), can be termed
“teaching materials”. On the contrary, items such as textbooks, lecture notes or tutorial sheets
generally are not to be termed “teaching materials”, if their content – discourse and treatise – is
discussed, rather than addressing the material itself, such as paper or compact disks.
Phrases such as “metallic materials” and “composite materials” have synonyms. “Metallic
materials” are in numerous publications termed “metals”, which violates the meaning of this term
established in chemistry. “Composite materials” are frequently termed “composites”, which is in
good concord with terminology that uses expression “ceramics” (for “ceramic materials”) and
“polymers” (for “polymeric materials”). In terms of applications, the overwhelming majority of
cases is related to solid forms of each of discussed categories. Thus, it is proposed herewith, to
introduce a new term “metallics”, which conforms well with terms “ceramics”, “polymers”,
“composites” and “solids”. It is recommended herewith abandoning lengthy homonyms such as
“metallic materials”, “composite materials”, “polymeric materials” and “ceramic materials”.
Metallics (n) are materials characterized by dominance of one or more metallic attributes, which is
usually the consequence of metals (i.e. metallic bond) occupying the largest fraction of the solid
structure; for example steels, bronze, gold, all belong to metallics.
Another example of the appropriate use of term “material”: "'The asphaltum contains an exactly
requisite amount of sulphides for production of rubber tires. This brown material also contains
ichthyol...’; Attribution: State of Utah, U.S. public relief program (1935-1943). Utah: A Guide to the
State (The WPA Guide to Utah), p.124, in »Mining«, Hastings House (1941) - Of a material found
near the Great Salt Lake.” [15]
iii) Term “Probability”
UNESCO Thesaurus [13,14] recommends use of term “Probability Theory”; it is unclear whether
“Probability Theory” is recommended instead using term “Probability”.
The WordNet [12] provides the following definition: “Probability (n) is a measure of how likely it is
that some event will occur; a number expressing the ratio of favorable cases to the whole number of
cases possible) ‘the probability that an unbiased coin will fall with the head up is 0.5’”. Use of
9
10. phrase “how likely” means explaining one term by simply introducing another term of the same
category (running in the loop, so-called circularity).
Therefore, the following definition of “probability” is proposed hereby: ‘Probability’ (n) is a
measure that can be used, in the absence of other measures, to define whether or not an event has, or
will happen (or is happening). For example, in the case of equally probable events, probability can
be quantified by means of the ratio 0 ≥ a/b ≤ 1,
where a = number of counted events for which the probability is to be established;
b = a + c (total number of events that can be counted in the observed ambient);
c = number of remaining optional events (equally probable) in the observed ambient.
The probability of an impossible event is 0, the probability of a certain (inevitable) event is 1.
Example: ‘The probability that an unbiased die will fall with a face showing two (spots) is 1/6”, see
Fig 1. Note: Theory of mathematical statistics provides comprehensive treatises defining probability.
Fig 1: A die: the number of spots
on each side varies from 1 to 6.
iv) Term ‘Element’
UNESCO Thesaurus [13, 14] distinguishes four descriptors:
- chemical element
- elementary particles
- structural elements (buildings)
- trace elements
and two additional usage phrases:
- elementary schools
- elementary education
So one certainly would not recommend usage 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 [12] 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".
The above WordNet use cited under (a) is promoted herewith as preferable use for this term. Use
presented under (b) above, can be substituted by terms ‘component’ or ‘constituent’. UNESCO
descriptors, such as ‘chemical element’ and ‘structural element’ are too lengthy, while the phrase
‘elementary education’ is too ambiguous.
10
11. v) Term ‘Bit’
UN/ECE (United Nations Centre for Trade Facilitation and Electronic Business)
TRADE/CEFACT/2005/24 Recommendation No. 20 - Units of Measure used in International Trade
Common Code in [14a] defines ‘bit’ as “a unit of information equal to one binary digit.” Other UN
published source [14b] provides definition of a ‘bit’ as “a binary digit that can assume a value of 0
or 1.” Both definitions concur to definition promoted in scientific discipline of Information Science.
The WordNet [12] provides 10 options. Apart from the above, the most differing meanings include:
a) Bit (n) is the cutting part of a drill; usually pointed and threaded and is replaceable in a brace or
bitstock or drill press; for example: "he looked around for the right size bit";
b) Bit (n) is an indefinitely short time; "wait just a moment"; "it only takes a minute"; "in just a bit".
This is a typical example of new-fashioned disciplines in demand usurping terms thus ignoring the
cultural heritage and increasing the language ambiguity. It is proposed herewith to decide
appropriate nomenclature by means of an informed and educated consensus.
Conclusions
In this Knowledge Age enhanced by artificial intelligence means, both communication speed and
misinformation waste, multiply at critical rates. Particularly obstructive is increase in information
entropy due to accumulation of homonyms and synonyms combined with other causes of ambiguity.
Universities appear to be institutions that carry the responsibility for initiating projects aiming at
disambiguation of scientific English language.
Artificial intelligence is invaluable in endeavors aiming at disambiguation of scientific and
engineering terminology but the human intelligence lays down superior criteria.
In addition, momentous efforts exhibited by approaching the problems of language prolixity,
ambiguity and translation by means of artificial intelligence, may be significantly reduced by virtue
of eliminating amassed homonyms and synonyms and by introducing more transparent definitions
of key terms by virtue of human intelligence.
Promoting a transparent, cross-disciplinary scientific and engineering terminology by means of
establishing a cross-disciplinary academic consortium will present significant contribution to
dissemination and broadening stock of knowledge. This coordinated effort must take in account
lexical heritage by means of intelligent and common sense consideration of historically established
use of English language.
Authors, editors and publishers would have a competent source of lexical references, and readers
would find such a lexis useful guide in their search for knowledge.
11
12. Reference Publications
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[2] The European University Association http://www.eua.be/eua/en/about_eua.jspx
(accessed on 13 October 2005)
[3] "Information on the Bologna Process" by Admissions Officers' and Credential Evaluators'
professional section of the EAIE - European Association for International Education
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[4] McCarty S "Cultural, Disciplinary and Temporal Contexts of e-Learning and English as a
Foreign Language", eLearn MAGAZINE published by ACM - Association for Computing
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Sept 2005)
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Press, London
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(accessed on 13 October 2005)
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Laboratory at Princeton University, under direction of G A Miller; http://wordnet.princeton.edu/
(accessed on 13 October 2005)
12
13. [13] The UNESCO Thesaurus, http://databases.unesco.org/thesaurus/ (accessed on 13 October
2005)
[14a] “UN glossaries UN interpreters’ resource page… “ http://un-interpreters.org/glossaries.html
& http://databases.unesco.org/thesaurus/other.html (accessed on 13 October 2005)
[14b] “Handbook on geographic information systems and digital mapping” Department of
Economic and Social Affairs, Statistics Division, Studies and Mehods, UN Publications, New York,
2000
[15] “The Columbia World of Quotations” Columbia University Press, 1996
[16] Spuzic S "An Initiative in Improving Knowledge Transfer in Engineering Education",
Proceedings from the 2nd Asia-Pacific Forum on Engineering and Technology Education, The
University of Sydney, 4-7 Jyly 1999, edited by Z Pudlowski, page 41
[17] "WordNet bibliography"; J Rosenzweig & R Mihalcea (Last update: September 11, 2004)
http://engr.smu.edu/~rada/wnb/ (accessed on 21 October 2005)
[18] O'Hara T and Wiebe J “Classifying functional relations in Factotum via WordNet hypernym
associations'' In: Proceedings of the 4th Intl. Conference on Intelligent Text Processing and
Computational Linguistics (CICLing-2003) , Mexico City, 2003
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