🎓Knowledge Management: Data, Information, Knowledge, Wisdom (Just the Quotes)

 "Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information upon it." (Samuel Johnson, 1775)

"It is almost as difficult to make a man unlearn his errors as his knowledge. Mal-information is more hopeless than non-information; for error is always more busy than ignorance. Ignorance is a blank sheet, on which we may write; but error is a scribbled one, on which we must first erase. Ignorance is contented to stand still with her back to the truth; but error is more presumptuous, and proceeds in the same direction. Ignorance has no light, but error follows a false one. The consequence is, that error, when she retraces her footsteps, has further to go, before she can arrive at the truth, than ignorance." (Charles C Colton, “Lacon”, 1820)

"In every branch of knowledge the progress is proportional to the amount of facts on which to build, and therefore to the facility of obtaining data." (James C Maxwell, [Letter to Lewis Campbell] 1851) 

"[The information of a message can] be defined as the 'minimum number of binary decisions which enable the receiver to construct the message, on the basis of the data already available to him.' These data comprise both the convention regarding the symbols and the language used, and the knowledge available at the moment when the message started." (Dennis Gabor, "Optical transmission" in Information Theory : Papers Read at a Symposium on Information Theory, 1952)

"Knowledge is not something which exists and grows in the abstract. It is a function of human organisms and of social organization. Knowledge, that is to say, is always what somebody knows: the most perfect transcript of knowledge in writing is not knowledge if nobody knows it. Knowledge however grows by the receipt of meaningful information - that is, by the intake of messages by a knower which are capable of reorganising his knowledge." (Kenneth E Boulding, "General Systems Theory - The Skeleton of Science", Management Science Vol. 2 (3), 1956)

"The idea of knowledge as an improbable structure is still a good place to start. Knowledge, however, has a dimension which goes beyond that of mere information or improbability. This is a dimension of significance which is very hard to reduce to quantitative form. Two knowledge structures might be equally improbable but one might be much more significant than the other." (Kenneth E Boulding, "Beyond Economics: Essays on Society", 1968)

"In perception itself, two distinct processes can be discerned. One is the gathering of the primary, sensory data or simple sensing of such things as light, moisture or pressure, and the other is the structuring of such data into information." (Edward Ihnatowicz, "The Relevance of Manipulation to the Process of Perception", 1977) 

"Data, seeming facts, apparent asso­ciations-these are not certain knowledge of something. They may be puzzles that can one day be explained; they may be trivia that need not be explained at all. (Kenneth Waltz, "Theory of International Politics", 1979)

"Knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone 'memorizes' information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"Information is data that has been given meaning by way of relational connection. This 'meaning' can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987) 

"Probabilities are summaries of knowledge that is left behind when information is transferred to a higher level of abstraction." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Network of Plausible, Inference", 1988)

"Information engineering has been defined with the reference to automated techniques as follows: An interlocking set of automated techniques in which enterprise models, data models and process models are built up in a comprehensive knowledge-base and are used to create and maintain data-processing systems." (James Martin, "Information Engineering, 1989)

"Knowledge is theory. We should be thankful if action of management is based on theory. Knowledge has temporal spread. Information is not knowledge. The world is drowning in information but is slow in acquisition of knowledge. There is no substitute for knowledge." (William E Deming, "The New Economics for Industry, Government, Education", 1993)

"Knowledge, truth, and information flow in networks and swarm systems. I have always been interested in the texture of scientific knowledge because it appears to be lumpy and uneven. Much of what we collectively know derives from a few small areas, yet between them lie vast deserts of ignorance. I can interpret that observation now as the effect of positive feedback and attractors. A little bit of knowledge illuminates much around it, and that new illumination feeds on itself, so one corner explodes. The reverse also holds true: ignorance breeds ignorance. Areas where nothing is known, everyone avoids, so nothing is discovered. The result is an uneven landscape of empty know-nothing interrupted by hills of self-organized knowledge." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995) 

"Now that knowledge is taking the place of capital as the driving force in organizations worldwide, it is all too easy to confuse data with knowledge and information technology with information." (Peter Drucker, "Managing in a Time of Great Change", 1995)

"Data is discrimination between physical states of things (black, white, etc.) that may convey or not convey information to an agent. Whether it does so or not depends on the agent's prior stock of knowledge." (Max Boisot, "Knowledge Assets", 1998)

"The unit of coding is the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon." (Richard Boyatzis, "Transforming qualitative information", 1998)

"While hard data may inform the intellect, it is largely soft data that generates wisdom." (Henry Mintzberg, "Strategy Safari: A Guided Tour Through The Wilds of Strategic Management", 1998)

"Information is just bits of data. Knowledge is putting them together. Wisdom is transcending them." (Ram Dass, "One-Liners: A Mini-Manual for a Spiritual Life (ed. Harmony", 2007)

"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon."(Judea Pearl, "Causal inference in statistics: An overview", Statistics Surveys 3, 2009)

"We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out picture is clear and accurate. But is it?" (Leonard Mlodinow, "The Drunkard's Walk: How Randomness Rules Our Lives", 2009) 

"We reach wisdom when we achieve a deep understanding of acquired knowledge, when we not only 'get it', but when new information blends with prior experience so completely that it makes us better at knowing what to do in other situations, even if they are only loosely related to the information from which our original knowledge came. Just as not all the information we absorb leads to knowledge, not all of the knowledge we acquire leads to wisdom." (Alberto Cairo, "The Functional Art", 2011)

"Any knowledge incapable of being revised with advances in data and human thinking does not deserve the name of knowledge." (Jerry Coyne, "Faith Versus Fact", 2015)

"The term data, unlike the related terms facts and evidence, does not connote truth. Data is descriptive, but data can be erroneous. We tend to distinguish data from information. Data is a primitive or atomic state (as in ‘raw data’). It becomes information only when it is presented in context, in a way that informs. This progression from data to information is not the only direction in which the relationship flows, however; information can also be broken down into pieces, stripped of context, and stored as data. This is the case with most of the data that’s stored in computer systems. Data that’s collected and stored directly by machines, such as sensors, becomes information only when it’s reconnected to its context."  (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"Real wisdom is not the knowledge of everything, but the knowledge of which things in life are necessary, which are less necessary, and which are completely unnecessary to know." (Lev N Tolstoy)

"The Information Age offers much to mankind, and I would like to think that we will rise to the challenges it presents. But it is vital to remember that information - in the sense of raw data - is not knowledge, that knowledge is not wisdom, and that wisdom is not foresight. But information is the first essential step to all of these." (Arthur C Clark)

🎓Knowledge Management: Domains (Just the Quotes)

"Great discoveries which give a new direction to currents of thoughts and research are not, as a rule, gained by the accumulation of vast quantities of figures and statistics. These are apt to stifle and asphyxiate and they usually follow rather than precede discovery. The great discoveries are due to the eruption of genius into a closely related field, and the transfer of the precious knowledge there found to his own domain." (Theobald Smith, Boston Medical and Surgical Journal Volume 172, 1915)

"Learning is any change in a system that produces a more or less permanent change in its capacity for adapting to its environment. Understanding systems, especially systems capable of understanding problems in new task domains, are learning systems." (Herbert A Simon, "The Sciences of the Artificial", 1968)

"A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system." (Humberto R Maturana, "Biology of Cognition", 1970)

"No theory ever agrees with all the facts in its domain, yet it is not always the theory that is to blame. Facts are constituted by older ideologies, and a clash between facts and theories may be proof of progress. It is also a first step in our attempt to find the principles implicit in familiar observational notions." (Paul K Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge", 1975)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure. […]  a person might use his cognitive map to derive explanations of the past, make predictions for the future, and choose policies in the present." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"The thinking person goes over the same ground many times. He looks at it from varying points of view - his own, his arch-enemy’s, others’. He diagrams it, verbalizes it, formulates equations, constructs visual images of the whole problem, or of troublesome parts, or of what is clearly known. But he does not keep a detailed record of all this mental work, indeed could not. […] Deep understanding of a domain of knowledge requires knowing it in various ways. This multiplicity of perspectives grows slowly through hard work and sets the state for the re-cognition we experience as a new insight." (Howard E Gruber, "Darwin on Man", 1981)

"Metaphor [is] a pervasive mode of understanding by which we project patterns from one domain of experience in order to structure another domain of a different kind. So conceived metaphor is not merely a linguistic mode of expression; rather, it is one of the chief cognitive structures by which we are able to have coherent, ordered experiences that we can reason about and make sense of. Through metaphor, we make use of patterns that obtain in our physical experience to organise our more abstract understanding." (Mark Johnson, "The Body in the Mind", 1987)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987)

"[…] a mental model is a mapping from a domain into a mental representation which contains the main characteristics of the domain; a model can be ‘run’ to generate explanations and expectations with respect to potential states. Mental models have been proposed in particular as the kind of knowledge structures that people use to understand a specific domain […]" (Helmut Jungermann, Holger Schütz & Manfred Thuering, "Mental models in risk assessment: Informing people about drugs", Risk Analysis 8 (1), 1988)

"Algorithmic complexity theory and nonlinear dynamics together establish the fact that determinism reigns only over a quite finite domain; outside this small haven of order lies a largely uncharted, vast wasteland of chaos." (Joseph Ford, "Progress in Chaotic Dynamics: Essays in Honor of Joseph Ford's 60th Birthday", 1988)

"When partitioning a domain, we divide the information model so that the clusters remain intact. [...] Each section of the information model then becomes a separate subsystem. Note that when the information model is partitioned into subsystems, each object is assigned to exactly one subsystem."  (Stephen J Mellor, "Object-Oriented Systems Analysis: Modeling the World In Data", 1988) 

"While a small domain (consisting of fifty or fewer objects) can generally be analyzed as a unit, large domains must be partitioned to make the analysis a manageable task. To make such a partitioning, we take advantage of the fact that objects on an information model tend to fall into clusters: groups of objects that are interconnected with one another by many relationships. By contrast, relatively few relationships connect objects in different clusters." (Stephen J Mellor, "Object-Oriented Systems Analysis: Modeling the World In Data", 1988) 

"A law explains a set of observations; a theory explains a set of laws. […] a law applies to observed phenomena in one domain (e.g., planetary bodies and their movements), while a theory is intended to unify phenomena in many domains. […] Unlike laws, theories often postulate unobservable objects as part of their explanatory mechanism." (John L Casti, "Searching for Certainty: How Scientists Predict the Future", 1990)

"Generally speaking, problem knowledge for solving a given problem may consist of heuristic rules or formulas that comprise the explicit knowledge, and past-experience data that comprise the implicit, hidden knowledge. Knowledge represents links between the domain space and the solution space, the space of the independent variables and the space of the dependent variables." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Inference is the process of matching current facts from the domain space to the existing knowledge and inferring new facts. An inference process is a chain of matchings. The intermediate results obtained during the inference process are matched against the existing knowledge. The length of the chain is different. It depends on the knowledge base and on the inference method applied." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"An individual understands a concept, skill, theory, or domain of knowledge to the extent that he or she can apply it appropriately in a new situation." (Howard Gardner, "The Disciplined Mind", 1999)

"Knowledge maps are node-link representations in which ideas are located in nodes and connected to other related ideas through a series of labeled links. They differ from other similar representations such as mind maps, concept maps, and graphic organizers in the deliberate use of a common set of labeled links that connect ideas. Some links are domain specific (e.g., function is very useful for some topic domains...) whereas other links (e.g., part) are more broadly used. Links have arrowheads to indicate the direction of the relationship between ideas." (Angela M. O’Donnell et al, "Knowledge Maps as Scaffolds for Cognitive Processing", Educational Psychology Review Vol. 14 (1), 2002) 

"We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse. One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior." (Stephen J Mellor, "Executable UML: A Foundation for Model-Driven Architecture", 2002)

"A domain model is not a particular diagram; it is the idea that the diagram is intended to convey. It is not just the knowledge in a domain expert’s head; it is a rigorously organized and selective abstraction of that knowledge." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Domain experts are usually not aware of how complex their mental processes are as, in the course of their work, they navigate all these rules, reconcile contradictions, and fill in gaps with common sense. Software can’t do this. It is through knowledge crunching in close collaboration with software experts that the rules are clarified, fleshed out, reconciled, or placed out of scope." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Effective domain modelers are knowledge crunchers. They take a torrent of information and probe for the relevant trickle. They try one organizing idea after another, searching for the simple view that makes sense of the mass. Many models are tried and rejected or transformed. Success comes in an emerging set of abstract concepts that makes sense of all the detail. This distillation is a rigorous expression of the particular knowledge that has been found most relevant." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Perception and memory are imprecise filters of information, and the way in which information is presented, that is, the frame, influences how it is received. Because too much information is difficult to deal with, people have developed shortcuts or heuristics in order to come up with reasonable decisions. Unfortunately, sometimes these heuristics lead to bias, especially when used outside their natural domains." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)

"This is always the case in analogical reasoning: Relations between two dissimilar domains never map completely to one another. In fact, it is often the salient similarities between the base and target domains that provoke thought and increase the usefulness of an analogy as a problem-solving tool." (Robbie T Nakatsu, "Diagrammatic Reasoning in AI", 2010)

"Conceptual models are best thought of as design-tools - a way for designers to straighten out and simplify the design and match it to the users’ task-domain, thereby making it clearer to users how they should think about the application. The designers’ responsibility is to devise a conceptual model that seems natural to users based on the users’ familiarity with the task domain. If designers do their job well, the conceptual model will be the basis for users’ mental models of the application." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"A model or conceptual model is a schematic or representation that describes how something works. We create and adapt models all the time without realizing it. Over time, as you gain more information about a problem domain, your model will improve to better match reality." (James Padolsey, "Clean Code in JavaScript", 2020)

"Knowledge graphs use an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle gives us an additional layer of organizing data (metadata) that adds connected context to support reasoning and knowledge discovery. […] Importantly, some processing can be done without knowledge of the domain, just by leveraging the features of the property graph model (the organizing principle)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

🚧Project Management: Success (Just the Quotes)

"Project management is needed only for situations which are out of the ordinary; but when the need exists, this may often be the only way by which the task may be handled successfully. These situations require a different attitude on the part of the top management, the undivided attention of a project manager and different methods for control and communications than those used in the normal routine business situation. […] Pure project management assigns complete responsibility for the task and resources needed for its accomplishment to one project manager. The organization of a large project, though it will be dissolved upon completion of the task, operates for its duration much like a regular division and is relatively independent of any other division or staff group." (Executive Sciences Institute, Operations Research/Management Science Vol 6, 1964)

"Basic to successful project management is recognizing when the project is needed - in other words, when to form a project, as opposed to when to use the regular functional organization to do the job." (David I Cleland & William R King, Systems Analysis and Project Management, 1968)

"Software projects fail for one of two general reasons: the project team lacks the knowledge to conduct a software project successfully, or the project team lacks the resolve to conduct a project effectively." (Steve C McConnell, "Software Project Survival Guide", 1997)

"Success in all types of organization depends increasingly on the development of customized software solutions, yet more than half of software projects now in the works will exceed both their schedules and their budgets by more than 50%." (Barry Boehm, "Software Cost Estimation with Cocomo II", 2000)

"Choosing a proper project strategy can mean the difference between success and failure." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"No project can succeed when the team members have no commitment to the plan, so the first rule of project planning is that the people who must do the work should help plan that part of the project. You will not only gain their commitment to the plan, but also most likely cover all of the important issues that you may individually have forgotten."(James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"Project failures are not always the result of poor methodology; the problem may be poor implementation. Unrealistic objectives or poorly defined executive expectations are two common causes of poor implementation. Good methodologies do not guarantee success, but they do imply that the project will be managed correctly." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"Success or failure of a project depends upon the ability of key personnel to have sufficient data for decision-making. Project management is often considered to be both an art and a science. It is an art because of the strong need for interpersonal skills, and the project planning and control forms attempt to convert part of the 'art' into a science." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"Successful software development is a team effort - not just the development team, but the larger team consisting of customer, management and developers. [...] Every software project needs to deliver business value. To be successful, the team needs to build the right things, in the right order, and to be sure that what they build actually works." (Ron Jeffries, "Extreme Programming Installed", 2001)

"The only truly successful project is the one that delivers what it is supposed to, gets results, and meets stakeholder expectations." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"A project is composed of a series of steps where all must be achieved for success. Each individual step has some probability of failure. We often underestimate the large number of things that may happen in the future or all opportunities for failure that may cause a project to go wrong. Humans make mistakes, equipment fails, technologies don't work as planned, unrealistic expectations, biases including sunk cost-syndrome, inexperience, wrong incentives, contractor failure, untested technology, delays, wrong deliveries, changing requirements, random events, ignoring early warning signals are reasons for delays, cost overruns and mistakes. Often we focus too much on the specific project case and ignore what normally happens in similar situations (base rate frequency of outcomes- personal and others)." (Peter Bevelin, "Seeking Wisdom: From Darwin to Munger", 2003)

"Risks and benefits always go hand in hand. The reason that a project is full of risk is that it leads you into uncharted waters. It stretches your capability, which means that if you pull it off successfully, it's going to drive your competition batty. The ultimate coup is to stretch your own capability to a point beyond the competition's ability to respond. This is what gives you competitive advantage and helps you build a distinct brand in the market." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

"Data migration is indeed a complex project. It is common for companies to underestimate the amount of time it takes to complete the data conversion successfully. Data quality usually suffers because it is the first thing to be dropped once the project is behind schedule. Make sure to allocate enough time to complete the task maintaining the highest standards of quality necessary. Migrate now, clean later typically leads to another source of mistrusted data, defeating the whole purpose of MDM." (Dalton Cervo & Mark Allen, "Master Data Management in Practice: Achieving true customer MDM", 2011)

"Stakeholder management to me is key, as success or failure is in the eye of the beholder. Time, cost and quality fall prey to the perceptions of the key stakeholders, who may have nothing to do with the running of the project." (Peter Parkes, "NLP for Project Managers", 2011)

"[...] consistently good project results are hard to come by, yet most organisations continue to think they’re doing a great job. It’s got to the stage where project failure has become so commonplace that we’ve started to see it as success, or we just aren’t seeing clearly at all." (Tony Martyr, "Why Projects Fail", 2018)

🎓Knowledge Management: Mental Model (Definitions)

"A mental model is a cognitive construct that describes a person's understanding of a particular content domain in the world." (John Sown, "Conceptual Structures: Information Processing in Mind and Machine", 1984)

"A mental model is a data structure, in a computational system, that represents a part of the real world or of a fictitious world." (Alan Granham, "Mental Models as Representations of Discourse and Text", 1987)

"[…] a mental model is a mapping from a domain into a mental representation which contains the main characteristics of the domain; a model can be ‘run’ to generate explanations and expectations with respect to potential states. Mental models have been proposed in particular as the kind of knowledge structures that people use to understand a specific domain […]" (Helmut Jungermann, Holger Schütz & Manfred Thuering, "Mental models in risk assessment: Informing people about drugs", Risk Analysis 8 (1), 1988)

 "A mental model is a knowledge structure that incorporates both declarative knowledge (e.g., device models) and procedural knowledge (e.g., procedures for determining distributions of voltages within a circuit), and a control structure that determines how the procedural and declarative knowledge are used in solving problems (e.g., mentally simulating the behavior of a circuit)." (Barbara Y White & John R Frederiksen, "Causal Model Progressions as a Foundation for Intelligent Learning Environments", Artificial Intelligence 42, 1990)

"’Mental models’ are deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action. [...] Mental models are deeply held internal images of how the world works, images that limit us to familiar ways of thinking and acting." (Peter Senge, "The Fifth Discipline”, 1990)

"[A mental model] is a relatively enduring and accessible, but limited, internal conceptual representation of an external system (historical, existing, or projected) [italics in original] whose structure is analogous to the perceived structure of that system." (James K Doyle & David N Ford, "Mental models concepts revisited: Some clarifications and a reply to Lane", System Dynamics Review 15 (4), 1999)

"In broad terms, a mental model is to be understood as a dynamic symbolic representation of external objects or events on the part of some natural or artificial cognitive system. Mental models are thought to have certain properties which make them stand out against other forms of symbolic representations." (Gert Rickheit & Lorenz Sichelschmidt, "Mental Models: Some Answers, Some Questions, Some Suggestions", 1999)

"A mental model is conceived […] as a knowledge structure possessing slots that can be filled not only with empirically gained information but also with ‘default assumptions’ resulting from prior experience. These default assumptions can be substituted by updated information so that inferences based on the model can be corrected without abandoning the model as a whole. Information is assimilated to the slots of a mental model in the form of ‘frames’ which are understood here as ‘chunks’ of knowledge with a well-defined meaning anchored in a given body of shared knowledge." (Jürgen Renn, “Before the Riemann Tensor: The Emergence of Einstein’s Double Strategy", 2005)

"A mental model is a mental representation that captures what is common to all the different ways in which the premises can be interpreted. It represents in 'small scale' how 'reality' could be - according to what is stated in the premises of a reasoning problem. Mental models, though, must not be confused with images." (Carsten Held et al, "Mental Models and the Mind", 2006)

"’Mental models’ are deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action." (Jossey-Bass Publishers, "The Jossey-Bass Reader on Educational Leadership”, 2nd Ed. 2007)

"A mental model is an internal representation with analogical relations to its referential object, so that local and temporal aspects of the object are preserved." (Gert Rickheit et al, "The concept of communicative competence" [in "Handbook of Communication Competence"], 2008)

"Internal representations constructed on the spot when required by demands of an external task or by a self-generated stimulus. It enables activation of relevant schemata, and allows new knowledge to be integrated. It specifies causal actions among concepts that take place within it, and it can be interacted with in the mind." (Daniel Churchill, "Mental Models" [in "Encyclopedia of Information Technology Curriculum Integration"] , 2008)

"Mental models are representations of reality built in people’s minds. These models are based on arrangements of assumptions, judgments, and values. A main weakness of mental models is that people’s assumptions and judgments change over time and are applied in inconsistent ways when building explanations of the world." (Luis F Luna-Reyes, "System Dynamics to Understand Public Information Technology", 2008)

"A mental model is the collection of concepts and relationships about the image of real world things we carry in our heads" (Hassan Qudrat-Ullah, "System Dynamics Based Technology for Decision Support", 2009)

"A mental recreation of the states of the world reproduced cognitively in order to offer itself as a basis for reasoning." (Eshaa M Alkhalifa, "Open Student Models", 2009)

[Shared Mental Model:] "A mental model that is shared among team members, and may include: 1) task-specific knowledge, 2) task-related knowledge, 3) knowledge of teammates and 4) attitudes/beliefs." (Rosemarie Reynolds et al, "Measuring Shared Mental Models in Unmanned Aircraft Systems", 2015) 

"A network of knowledge content, as well as the relationships among the content."(Rosemarie Reynolds et al, "Measuring Shared Mental Models in Unmanned Aircraft Systems", 2015)

"A mental model (aka mental representation/image/picture) is a mental structure that attempts to model (depict, imagine) how real or imaginary things look like, work or fit together." (The Web of Knowledge) [source]

Resources:
Quotes on "Mental Models" at the-web-of-knowledge.blogspot.com.

🎓Knowledge Management: Concept Map (Definitions)

"Concept maps are built of nodes connected by connectors, which have written descriptions called linking phrases instead of polarity of strength. Concept maps can be used to describe conceptual structures and relations in them and the concept maps suit also aggregation and preservation of knowledge" (Hannu Kivijärvi et al, "A Support System for the Strategic Scenario Process", 2008) 

"A hierarchal picture of a mental map of knowledge." (Gregory MacKinnon, "Concept Mapping as a Mediator of Constructivist Learning", 2009)

"A tool that assists learners in the understanding of the relationships of the main idea and its attributes, also used in brainstorming and planning." (Diane L Judd, "Constructing Technology Integrated Activities that Engage Elementary Students in Learning", 2009)

"Concept maps are graphical knowledge representations that are composed to two components: (1) Nodes: represent the concepts, and (2) Links: connect concepts using a relationship." (Faisal Ahmad et al, "New Roles of Digital Libraries", 2009)

"A concept map is a diagram that depicts concepts and their hierarchical relationships." (Wan Ng & Ria Hanewald, "Concept Maps as a Tool for Promoting Online Collaborative Learning in Virtual Teams with Pre-Service Teachers", 2010)

"A diagram that facilitates organization, presentation, processing and acquisition of knowledge by showing relationships among concepts as node-link networks. Ideas in a concept map are represented as nodes and connected to other ideas/nodes through link labels." (Olusola O Adesope & John C Nesbit, "A Systematic Review of Research on Collaborative Learning with Concept Maps", 2010)

"A visual construct composed of encircled concepts (nodes) that are meaningfully inter-connected by descriptive concept links either directly, by branch-points (hierarchies), or indirectly by cross-links (comparisons). The construction of a concept map can serve as a tool for enhancing communication, either between an author and a student for a reading task, or between two or more students engaged in problem solving. (Dawndra Meers-Scott, "Teaching Critical Thinking and Team Based Concept Mapping", 2010)

"Are graphical ways of working with ideas and presenting information. They reveal patterns and relationships and help students to clarify their thinking, and to process, organize and prioritize. The visual representation of information through word webs or diagrams enables learners to see how the ideas are connected and understand how to group or organize information effectively." (Robert Z Zheng & Laura B Dahl, "Using Concept Maps to Enhance Students' Prior Knowledge in Complex Learning", 2010)

"Concept maps are hierarchical trees, in which concepts are connected with labelled, graphical links, most general at the top." (Alexandra Okada, "Eliciting Thinking Skills with Inquiry Maps in CLE", 2010)

"One powerful knowledge presentation format, devised by Novak, to visualize conceptual knowledge as graphs in which the nodes represent the concepts, and the links between the nodes are the relationships between these concepts." (Diana Pérez-Marín et al, "Adaptive Computer Assisted Assessment", 2010)

"A form of visualization showing relationships among concepts as arrows between labeled boxes, usually in a downward branching hierarchy." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A graphical depiction of relationships ideas, principals, and activities leading to one major theme." (Carol A Brown, "Using Logic Models for Program Planning in K20 Education", 2013)

"A diagram that presents the relationships between concepts." (Gwo-Jen Hwang, "Mobile Technology-Enhanced Learning", 2015)

"A graphical two-dimensional display of knowledge. Concepts, usually presented within boxes or circles, are connected by directed arcs that encode, as linking phrases, the relationships between the pairs of concepts." (Anna Ursyn, "Visualization as Communication with Graphic Representation", 2015)

"A graphical tool for representing knowledge structure in a form of a graph whose nodes represent concepts, while arcs between nodes correspond to interrelations between them." (Yigal Rosen & Maryam Mosharraf, "Evidence-Centered Concept Map in Computer-Based Assessment of Critical Thinking", 2016) 

"Is a directed graph that shows the relationship between the concepts. It is used to organize and structure knowledge." (Anal Acharya & Devadatta Sinha, "A Web-Based Collaborative Learning System Using Concept Maps: Architecture and Evaluation", 2016)

"A graphic depiction of brainstorming, which starts with a central concept and then includes all related ideas." (Carolyn W Hitchens et al, "Studying Abroad to Inform Teaching in a Diverse Society", 2017)

"A graphic visualization of the connections between ideas in which concepts (drawn as nodes or boxes) are linked by explanatory phrases (on arrows) to form a network of propositions that depict the quality of the mapper’s understanding" (Ian M Kinchin, "Pedagogic Frailty and the Ecology of Teaching at University: A Case of Conceptual Exaptation", 2019)

"A diagram in which related concepts are linked to each other." (Steven Courchesne &Stacy M Cohen, "Using Technology to Promote Student Ownership of Retrieval Practice", 2020)

🎓Knowledge Management: Conceptual Model (Definitions)

"A conceptual model is a qualitative description of the system and includes the processes taking place in the system, the parameters chosen to describe the processes, and the spatial and temporal scales of the processes." (A Avogadro & R C Ragaini, "Technologies for Environmental Cleanup", 1993)

"A conceptual model is a model of the projected system that is independent of implementation details." (Michael Worboys, "GIS: A Computing Perspective", 1995)

"A conceptual model is what in the model theory is called a set of formulas making statements about the world." (Dickson Lukose [Eds.], "Conceptual Structures: Fulfilling Peirce's Dream" Vol 5, 1997)

"A conceptual model is a representation of the system expertise using this formalism. An internal model is derived from the conceptual model and from a specification of the system transactions and the performance constraints." (Zbigniew W. Ras & Andrzej Skowron [Eds.], Foundations of Intelligent Systems: 10th International Symposium Vol 10, 1997)

"A conceptual model is one which reflects reality by placing words which are concepts into the model in the same way that the model aeroplane builder puts wings, a fuselage, and a cockpit together." (Lynn Basford & ‎Oliver Slevin, "Theory and Practice of Nursing: An Integrated Approach to Caring Practice", 2003) 

"A conceptual model is simply a framework or schematic to understand the interaction of workforce education and development systems with other variables in a society." (Jay W Rojewski, "International Perspectives on Workforce Education and Development", 2004) 

"A conceptual model is a mental image of a system, its components, its interactions. It lays the foundation for more elaborate models, such as physical or numerical models. A conceptual model provides a framework in which to think about the workings of a system or about problem solving in general. An ensuing operational model can be no better than its underlying conceptualization." (Henry N Pollack, "Uncertain Science … Uncertain World", 2005)

"A particular kind of learning object design to be supplied to learners to support their mental modeling." (Daniel Churchill, "Mental Models" [in "Encyclopedia of Information Technology Curriculum Integration"], 2008)

"The concepts and constructs about real work things we have in our heads are called mental model." (Hassan Qudrat-Ullah, "System Dynamics Based Learning Environments" [in "Encyclopedia of Information Technology Curriculum Integration"], 2008)

"Representations of real or imaginary structure in the human mind enabling orientation as well as goal orientated actions and movements" (Ralf Wagner, "Customizing Multimedia with Multi-Trees" [in "Encyclopedia of Multimedia Technology and Networking" 2nd Ed.], 2009)

"A conceptual model is a qualitative description of 'some aspect of the behaviour of a natural system'. This description is usually verbal, but may also be accompanied by figures and graphs." (Howard S. Wheater et al., "Groundwater Modelling in Arid and Semi-Arid Areas, 2010) 

"[…] a conceptual model is a diagram connecting variables and constructs based on theory and logic that displays the hypotheses to be tested." (Mary Wolfinbarger Celsi et al, "Essentials of Business Research Methods", 2011) 

"A conceptual model of an interactive application is, in summary: the structure of the application - the objects and their operations, attributes, and relation-ships; an idealized view of the how the application works – the model designers hope users will internalize; the mechanism by which users accomplish the tasks the application is intended to support." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"Simply put, a conceptual model is a simplified representation of reality, devised for a certain purpose and seen from a certain point of view."(David W Emble & Bernhard Thalheim, "Handbook of Conceptual Modeling", 2012) 

"Briefly, a conceptual model is the configuration of conceptual elements and the navigation between them. As such, a conceptual model is the foundation of the user interface of any interactive system." (Avi Parush, "Conceptual Design for Interactive Systems", 2015)

"A conceptual model is a framework that is initially used in research to outline the possible courses of action or to present an idea or thought. When a conceptual model is developed in a logical manner, it will provide a rigor to the research process." (N Elangovan & R Rajendran, "Conceptual Model: A Framework for Institutionalizing the Vigor in Business Research", 2015) 

"A model or conceptual model is a schematic or representation that describes how something works. We create and adapt models all the time without realizing it. Over time, as you gain more information about a problem domain, your model will improve to better match reality." (James Padolsey, "Clean Code in JavaScript", 2020)

Resources:
Quotes on "Conceptual Models" at the-web-of-knowledge.blogspot.com.

🎓Knowledge Management: Cognitive Map (Definitions)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"A cognitive map is the representation of thinking about a problem that follows from the process of mapping." (Colin Eden, "Analyzing cognitive maps to help structure issues or problems", 2002)

"A mental representation of a portion of the physical environment and the relative locations of points within it." (Andrew M Colman, "A Dictionary of Psychology" 3rd Ed, 2008)

"A mental model (or map) of the external environment which may be constructed following exploratory behaviour." (Michael Allaby, "A Dictionary of Zoology" 3rd Ed., 2009)

"An FCM [Fuzzy Cognitive Map] is a directed graph with concepts like policies, events etc. as nodes and causalities as edges. It represents causal relationship between concepts." (Florentin Smarandache &  W B Vasantha Kandasamy, "Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps", 2014)

"A conceptual tool that provides a representation of particular natural or social environments in the form of a model." (Evangelos C Papakitsos et al, "The Challenges of Work-Based Learning via Systemic Modelling in the European Union", 2020)

"A representation of the conceptualization that the subject constructs of the system in which he evolves. The set of cognitive representations that emerge make it possible to understand his actions, the links between the factors structuring the cognitive patterns dictating his behaviors." (Henda E Karray & Souhaila Kammoun, "Strategic Orientation of the Managers of a Tunisian Family Group Before and After the Revolution", 2020)

"A cognitive map is a type of mental representation which serves an individual to acquire, code, store, recall, and decode information about the relative locations and attributes of phenomena in their everyday or metaphorical spatial environment." (Wikipedia) [source]

🎓Knowledge Management: Tacit Knowledge (Definitions)

"Know-how that is difficult to articulate and share; intuition or skills that cannot easily be put into words." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"The domain of subjective, cognitive, and experimental knowledge that is highly personal and difficult to formalize." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The knowledge that a person retains in their mind. It is relatively hard to transfer to others and to disseminate widely. Also known as implicit knowledge." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Knowledge that is based on experience and not easy to share, document, or explain." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Knowledge that resides in people's heads. Also referred to as know-how, rules of thumb, or heuristics." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"Tacit knowledge is sometimes referred to as knowledge inside people’s heads. It includes the skills and intuition that experienced people apply as a matter of course in their work. Tacit knowledge is contrasted with explicit knowledge, which is knowledge that is documented in a sharable form. One of the goals of knowledge management is to enable tacit knowledge to be shared by making it explicit knowledge." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Undocumented information." (Project Management Institute, "Software Extension to the PMBOK® Guide 5th Ed", 2013)

"Personal knowledge that can be difficult to articulate and share such as beliefs, experience, and insights." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

🎓Knowledge Management: Taxonomy (Definitions)

"A classification system." (Ruth C Clark & Chopeta Lyons, "Graphics for Learning", 2004)

"A hierarchical structure within which related items are organized, classified, or categorized, thus illustrating the relationships between them." (Richard Caladine, "Taxonomies for Technology", 2008)

"A taxonomy is a hierarchical structure displaying parent-child relationships (a classification). A taxonomy extends a vocabulary and is a special case of a the more general ontology." (Troels Andreasen & Henrik Bulskov, "Query Expansion by Taxonomy", 2008)

"An orderly classification that explicitly expresses the relationships, usually hierarchical (e.g., genus/species, whole/part, class/instance), between and among the things being classified." (J P Getty Trust, "Introduction to Metadata" 2nd Ed., 2008)

"This term traditionally refers to the study of the general principles of classification. It is widely used to describe computer-based systems that use hierarchies of topics to help users sift through information." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"A kind of classification method which organizes all kinds of things into predefined hierarchical structure." (Yong Yu et al, "Social Tagging: Properties and Applications", 2010)

"Any system of categories used to organize something, including documents, often less comprehensive than a thesaurus." (Steven Woods et al, "Knowledge Dissemination in Portals", 2011)

"Generally, a collection of controlled vocabulary terms organized into a structure of parent-child relationships. Each term is in at least one relationship with another term in the taxonomy. Each parent's relationship with all of its children are of only one type (whole-part, genus-species, or type-instance). The addition of associative relationships creates a thesaurus." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A definitional hierarchy of concepts. Traditional taxonomies are tree-structured (a concept is assumed to have exactly one superconcept and multiple subconcepts). The higher a concept is positioned in the definitional hierarchy, the more individuals it describes (the comprehension of the concept), but the less definitional properties are needed (the meaning of a concept)." (Marcus Spies & Said Tabet, "Emerging Standards and Protocols for Governance, Risk, and Compliance Management", 2012) 

"A hierarchical representation of metadata. The top level is the category, and each subsequent level provides a refinement (detail) of the top-level term." (Charles Cooper & Ann Rockley, "Managing Enterprise Content: A Unified Content Strategy" 2nd Ed., 2012)

"A hierarchical structure of information components, for example, a subject, business–unit, or functional taxonomy, any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Managing Electronic Records: Methods, Best Practices, and Technologies", 2013)

"A classification of text" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A hierarchical structure of information components (e.g., a subject, business unit, or functional taxonomy), any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"provides context within the ontology. Taxonomies are used to capture hierarchical relationships between elements of interest. " (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Taxonomy is the science and practice of classification. Taxonomies are used when categorizing real-life as well as artificial phenomenon and the aim is to make systematic studies easier." (Ulf Larson et al, "Guidance for Selecting Data Collection Mechanisms for Intrusion Detection", 2015)

"A taxonomy is a hierarchy that is created by a set of interconnected class inclusion relationship." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"A hierarchical structure of information components, for example, a subject, business unit, or functional taxonomy, any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Information Governance for Healthcare Professionals", 2018)

🎓Knowledge Management: Ontology (Definitions)

"A data model that represents the entities that are defined and evaluated by its own attributes, and organized according to a hierarchy and a semantic. Ontologies are used for representing knowledge on the whole of a specific domain or on of it." (Gervásio Iwens et al, "Programming Body Sensor Networks", 2008)

"An ontology specifies a conceptualization, that is, a structure of related concepts for a given domain." (Troels Andreasen & Henrik Bulskov, "Query Expansion by Taxonomy", 2008)

"A semantic structure useful to standardize and provide rigorous definitions of the terminology used in a domain and to describe the knowledge of the domain. It is composed of a controlled vocabulary, which describes the concepts of the considered domain, and a semantic network, which describes the relations among such concepts. Each concept is connected to other concepts of the domain through semantic relations that specify the knowledge of the domain. A general concept can be described by several terms that can be synonyms or characteristic of different domains in which the concept exists. For this reason the ontologies tend to have a hierarchical structure, with generic concepts/terms at the higher levels of the hierarchy and specific concepts/terms at the lover levels, connected by different types of relations." (Mario Ceresa, "Clinical and Biomolecular Ontologies for E-Health", Handbook of Research on Distributed Medical Informatics and E-Health, 2009)

"In the context of knowledge sharing, the chapter uses the term ontology to mean a specification of conceptual relations. An ontology is the concepts and relationships that can exist for an agent or a community of agents. The chapter refers to designing ontologies for the purpose of enabling knowledge sharing and re-use." (Ivan Launders, "Socio-Technical Systems and Knowledge Representation", 2009)

 "The systematic description of a given phenomenon, which often includes a controlled vocabulary and relationships, captures nuances in meaning and enables knowledge sharing and reuse. Typically, ontology defines data entities, data attributes, relations and possible functions and operations." (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)

"Those things that exist are those things that have a formal representation within the context of a machine. Knowledge commits to an ontology if it adheres to the structure, vocabulary and semantics intrinsic to a particular ontology i.e. it conforms to the ontology definition. A formal ontology in computer science is a logical theory that represents a conceptualization of real world concepts." (Philip D. Smart, "Semantic Web Rule Languages for Geospatial Ontologies", 2009)

"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." (Yong Yu et al, "Social Tagging: Properties and Applications", 2010)

"Is set of well-defined concepts describing a specific domain." (Hak-Lae Kim et al, "Representing and Sharing Tagging Data Using the Social Semantic Cloud of Tags", 2010)

"An ontology is a 'formal, explicit specification of a shared conceptualisation'. It is composed of concepts and relations structured into hierarchies (i.e. they are linked together by using the Specialisation/Generalisation relationship). A heavyweight ontology is a lightweight ontology (i.e. an ontology simply based on a hierarchy of concepts and a hierarchy of relations) enriched with axioms used to fix the semantic interpretation of concepts and relations." (Francky Trichet et al, "OSIRIS: Ontology-Based System for Semantic Information Retrieval and Indexation Dedicated to Community and Open Web Spaces", 2011)

"The set of the things that can be dealt with in a particular domain, together with their relationships." (Steven Woods et al, "Knowledge Dissemination in Portals", 2011) 

"In semantic web and related technologies, an ontology (aka domain ontology) is a set of taxonomies together with typed relationships connecting concepts from the taxonomies and, possibly, sets of integrity rules and constraints defining classes and relationships." (Marcus Spies & Said Tabet, "Emerging Standards and Protocols for Governance, Risk, and Compliance Management", 2012)

"High-level knowledge and data representation structure. Ontologies provide a formal frame to represent the knowledge related with a complex domain, as a qualitative model of the system. Ontologies can be used to represent the structure of a domain by means of defining concepts and properties that relate them." (Lenka Lhotska et al, "Interoperability of Medical Devices and Information Systems", 2013)

"(a) In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about concepts. (b) In philosophy, ontology is the study of the nature of being, becoming, existence , or reality , as well as the basic categories of being and their relations. Traditionally listed as a part of the major branch of philosophy known as metaphysics, ontology deals with questions concerning what entities exist or can be said to exist, and how such entities can be grouped, related within a hierarchy, and subdivided according to similarities and differences." (Ronald J Lofaro, "Knowledge Engineering Methodology with Examples", 2015)

"It is a shared structure which classify and organizes all the entities of a given domain." (T R Gopalakrishnan Nair, "Intelligent Knowledge Systems", 2015)

"The study of how things relate. Used in big data to analyze seemingly unrelated data to discover insights." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"An ontology is a formal, explicit specification of a shared conceptualization." (Fu Zhang et al, "A Review of Answering Queries over Ontologies Based on Databases", 2016)

🎓Knowledge Management: Explicit Knowledge (Definitions)

"Knowledge that is easily codified, shared, documented, and explained." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The knowledge that deals with objective, rational, and technical knowledge (data, policies, procedures, software, documents, etc.)." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Explicit knowledge is information that is captured in a way that it can be shared. People can learn it without having to rely directly on other people. In knowledge management practice, explicit knowledge is contrasted with tacit knowledge, which is knowledge that is inside people’s heads." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2012)

"Recorded information, for example, a written policy or procedure." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"Knowledge that can be codified using symbols such as words, numbers, and pictures." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

🎓Knowledge Management: Knowledge Management (Definitions)

"The conscious and systematic facilitation of knowledge creation or development, diffusion or transfer, safeguarding, and use at the individual, team- and organizational level." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"The field of study that relates to the centralized management of a company’s corporate knowledge and information assets in order to provide this knowledge to as many company staff members as possible and thus encourage better and more consistent decision making." (Evan Levy & Jill Dyché, "Customer Data Integration", 2006)

"Discipline that intends to provide, at its most suitable level, the accurate information and knowledge for the right people, whenever they may be needed and at their best convenience." (J Ares, "Guidelines for Deploying a Knowledge Management System", 2008)

"The process of creating, capturing and organizing knowledge objects. A knowledge object might be a research report, a budget for the development of a new product, or a video presentation. Knowledge Management programs seek to capture objects in a repository that is searchable and accessible in electronic form." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"The process established to capture and use specific knowledge in an organization for the purpose of improving organizational performance." (Murray E Jennex, "Technologies in Support of Knowledge Management Systems", 2009)

"1.The management of an environment where people generate tacit knowledge, render it into explicit knowledge, and feed it back to the organization. The cycle forms a base for more tacit knowledge, which keeps the cycle going in an intelligent learning organization. (Brackett 2011) 2.The discipline that fosters organizational learning and the management of intellectual capital as an enterprise resource." (DAMA International, "The DAMA Dictionary of Data Management" 1st Ed., 2010)

"The process that helps organizations identify, select, organize, disseminate, and transfer important information and expertise that are part of the organization's memory and that may reside in unstructured form within the organization." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Knowledge management is a set of practices related to how organizations learn from their own experiences. Many of these practices focus on ensuring that what employees know and learn is captured in a shareable form (explicit knowledge)." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The accumulation, organization, and use of experience and lessons learned, which can be leveraged to improve future decision-making efforts. KM often involves listing and indexing subject matter experts, project categories, reports, studies, proposals, and other intellectual property sources or outputs that are retained to build corporate memory. Good KM systems help train new employees and reduce the impact of turnover and retirement of key employees." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"The process of capturing, using, leveraging, and sharing organizational knowledge." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"The intentional process of creation, acquisition and sharing of knowledge and its utilization as a key factor in the creation of added value. It is an inextricably human and cooperative process." (António C Moreira & Ricardo A Zimmermann, "Electronic Government: Challenges for Public Services Consumer Behaviour and Value Creation", 2015)

"Knowledge management is considered as a systematic process of managing knowledge assets, processes, and environment to facilitate the creation, organization, sharing, utilization, and measurement of knowledge to achieve the strategic aims of an organization." (Haitham Alali et al, "Knowledge Sharing Success Model of Virtual Communities of Practice in Healthcare Sector", 2016)

"Knowledge management promotes activities and processes to acquire, create, document, and share formal explicit knowledge and informal implicit knowledge. Knowledge management involves identifying a group of people who have a need to share knowledge, developing technological support that enables knowledge sharing, and creating a process for transferring and disseminating knowledge." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"The process of creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieving organizational objectives by making the best use of knowledge." (Izabella V Lokshina et al, "Internet of Things and Big Data-Driven Data Analysis Services for Third Parties", 2019)

"The methods and underlying policies for sharing information effectively so that the sum of the skills, experience and entrepreneurial attributes of all stakeholders is greater than the sum of the individual parts. If done well, each stakeholder also benefits, thus increasing the ‘sum of the individual parts’ that go on to increase the ‘sum of the whole’ in a virtuous circle." (Sue Milton, "Data Privacy vs. Data Security", 2021)

🎓Knowledge Management: Knowledge (Definitions)

"Justified true belief, the know-what/-how/-who/-why that individuals use to solve problems, make predictions or decisions, or take actions." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"An individual’s understanding of facts or information. Knowledge provides the basis for performing a skill that an individual must have to perform a task successfully." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management" 2nd Ed., 2009)

"1.Generally, expertise; familiarity gained through experience or association; cognizance, the fact or condition of knowing something; the acquaintance with or the understanding of something; the fact or condition of being aware of something, of apprehending truth or fact." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The body of information and facts about a specific subject. Knowledge implies familiarity, awareness, and understanding of information as it applies to an environment. A key characteristic of knowledge is that 'new' knowledge can be derived from 'old' knowledge." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"The fact or condition of knowing something with familiarity gained through experience or association. Knowledge adds understanding and retention to information." (Craig S Mullins, "Database Administration", 2012)

"The metadata about all the changes that a participant has seen and maintains." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A collection of specialized facts, procedures, and judgment rules. Knowledge refers to what one knows and understands. Knowledge is categorized as unstructured, structured, explicit, or implicit. What we know we know we call explicit knowledge. Knowledge that is unstructured and understood, but not clearly expressed, we call implicit knowledge." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence 3rd Ed.", 2017)

"A mixture of experience, values and beliefs, contextual information, intuition, and insight that people use to make sense of new experiences and information." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide)", 2017)

"Knowing something with the familiarity gained through experience, education, observation, or investigation; it is understanding a process, practice, or technique, or how to use a tool." (Project Management Institute, "Project Manager Competency Development Framework" 3rd Ed., 2017)

"That array of facts and relationships that an individual has available to him or her for the performance of work, it may be part or all of an accepted body of knowledge, or knowledge that has been produced as largely self-generated content by the individual." (Catherine Burke et al, "Systems Leadership" 2nd Ed., 2018)

"The sum of a person’s - or mankind’s - information about and ability to understand the world." (Open Data Handbook)

🔦Process Management: Roles (Definitions)

"A job type defined in terms of a set of responsibilities." (Atul Apte, "Java Connector Architecture: Building Custom Connectors and Adapters", 2002)

"A set of expectations for behavior; describes the extent to which each individual performs activities related to project." (Timothy J  Kloppenborg et al, "Project Leadership", 2003)

"Specified responsibilities that identify a set of related activities to be performed by a designated individual (e.g., a project manager)." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A definition of the behavior and responsibilities of an individual or set of individuals working together as a team." (Bruce MacIsaac & Per Kroll, "Agility and Discipline Made Easy: Practices from OpenUP and RUP", 2006)

"A defined set of work tasks, dependencies, and responsibilities that can be assigned to an individual as a work package. A role describes a collection of tasks that constitute one component of a process, and would normally be performed by an individual." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management" 2nd Ed., 2009)

"The set of expectations in a social system that define the services individuals or groups are supposed to provide." (Alexander Grashow et al, "The Practice of Adaptive Leadership", 2009)

"The characteristic and expected behaviors of an individual, derived from his or her responsibilities and preferences in providing value to the organization." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"1.Generally, a label assigned to a set of connected behaviors, rights and obligations. 2.In data modeling, the way in which entities of one type relate to entities of another type in a relationship. 3.In data security, a name used to refer to the logical set of related responsibilities assignable to a person or organization, and to parties with these assigned responsibilities." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A defined function to be performed by a project team member, such as testing, filing, inspecting, coding." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"Description of specific skills, qualifications and work profiles in software development. These should be filled by the persons (responsible for these roles) in the project." (Tilo Linz et al, "Software Testing Foundations" 4th Ed., 2014)

"Usual or expected functionality of an actor in the context of an activity or a business process; an actor can have one or several roles. " (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"A defined function to be performed by a project team member, such as testing, filing, inspecting, or coding." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide )", 2017)

"In ITIL, this is a set of responsibilities, activities and authorities granted to a person or team. A role is defined in a process. One person or team may have multiple roles, for example the roles of configuration manager and change manager may be carried out by a single person." (Brian Johnson & Leon-Paul de Rouw, "Collaborative Business Design", 2017)

"A defined function to be performed by a project team member, such as testing, filing, inspecting, coding." (Jeffrey K Pinto, "Project Management: Achieving Competitive Advantage" 5th Ed., 2018)

"A set of responsibilities, activities and authorities granted to a person/team." (ITIL)

🔦Process Management: Baseline (Definitions)

"A documented characterization of the actual results achieved by following a process, which is used as a benchmark for comparing actual process performance against expected process performance." (Sandy Shrum et al, "CMMI: Guidelines for Process Integration and Product Improvement", 2003)

"A range of expected results that would normally be achieved by following a defined process. Often expressed in terms of the process control limits defined by the discipline of statistical process control." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"Documented process performance values used as a reference to compare actual and expected process performance." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A documented characterization of the range of expected results that would normally be achieved by following a specific process under typical circumstances." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management" 2nd Ed., 2009)

"A documented characterization of the results achieved by following a process that is used as a benchmark for comparing actual process performance against expected process performance." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management" 2nd Ed., 2009)

[capability baseline:] "A statistically based description of the performance or results of a process that has been performed repeatedly. Capability baselines can quantify attributes of the process (e.g., effort or duration) or of the product produced by the process (e.g., amount or quality). Control charts used in statistical process control are one form of capability baseline. However, other statistical representations may be more appropriate, depending on the nature of the data being characterized. The purpose of a capability baseline is to predict outcomes and to interpret the results of process performance." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management" 2nd Ed., 2009)