The Knowledge Pyramid: A Critique of the DIKW Hierarchy This paper has been accepted for publication in Journal of Information Science and the final (edited, revid and typet) version of this paper will be published in Journal of Information Science Vol?/Issue?/Year? By SAGE Publications Ltd.. All rights rerved. © CILIP. For more information plea visit: uk
Martin Frické
School of Information Resources and Library Science, The University of Arizona, Tucson, Arizona, AZ 85718, USA Abstract
The paper evaluates the Data-Information-Knowledge-Wisdom (DIKW) Hierarchy. This hierarchy is part of the canon of information science and management. The paper considers whether the hierarchy, also known as the ‘Knowledge Hierarchy’, is a uful and intellectually desirable construct to introduce, whether the views expresd about DIKW are true and have evidence in favour of them, and whether there are good reasons offered or sound assumptions made about DIKW. Arguments are offered that the hierarchy is unsound and methodologically undesirable. The paper identifies a central logical error t
hat DIKW makes. The paper identifies the dated and unsatisfactory philosophical positions of operationalism and inductivism as the philosophical backdrop to the hierarchy. The paper concludes with a sketch of some positive theories, of value to information science, on the nature of the components of the hierarchy: that data is anything recordable in a mantically and pragmatically sound way, that information is what is known in other literature as ‘weak knowledge’, that knowledge also is ‘weak knowledge’ and that wisdom is the posssion and u, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge.
Keywords data; information; knowledge; wisdom, the DIKW Hierarchy, the Knowledge Hierarchy, the Knowledge Pyramid.
国外旅游景点1.Introduction
Many theoreticians, in Computer Science, Management Information Systems and in Librarianship, e information in terms of a data-information-knowledge–wisdom (DIKW) hierarchy or pyramid [1] [2].
As Rowley writes
The hierarchy referred to variously as the ‘Knowledge Hierarchy’, the ‘Information
Hierarchy’ and the ‘Knowledge Pyramid’ is one of the fundamental, widely recognized and
‘taken-for-granted’ models in the information and knowledge literatures. It is often quoted,
or ud implicitly, in definitions of data, information and knowledge in the information
management, information systems and knowledge management literatures (1)
Rowley [1] offers a detailed exegesis of just how widespread this view is, and of the similarities and differences between the writers’ statements. (There also has been a wide-ranging discussion on the JESSE listrv [3].) The prent paper aims to complement this work. The targets here are the questions of whether DIKW is a uful and intellectually desirable construct to introduce, whether the
views expresd about DIKW are true and have evidence in favour of them, and whether there are good reasons offered or sound assumptions made about DIKW. In brief, is DIKW an intellectually attractive prospect?
The answer to be defended here is that the DIKW pyramid should be abandoned. It should no longer be part of the canon of information science, and such related disciplines as systems theory, information management, information systems, knowledge management, and library and documentation science. Discarding DIKW would leave an intellectual and theoretical vacuum over the nature of data, information, knowledge, and wisdom, and their interrelationships, if any. This paper does not attempt to replace the pyramid with another structure. It does, though, offer some positive suggestions on the nature of data, information, knowledge, and wisdom. The concepts, certainly the first three, are part of the common currency of information science. Thus, the paper attempts to advance debate on the theoretical underpinnings of information science.
2.What is DIKW?
As Rowley’s work testifies, there are genuine and possibly substantive differences in view about DIKW and its properties. Nevertheless, there is a core, and sufficient similarities in view for a positio
n to be extracted and scrutinized. The main views are perhaps best expresd in the traditional sources of Adler, Ackoff and Zeleny [4-6].
What, at the heart, is DIKW and how does it work? It is suggested that there is a hierarchy built on the foundation of data [4], p 3.
Wisdom is located at the top of a hierarchy of types …. Descending from wisdom there are
understanding, knowledge, information, and, at the bottom, data. Each of the includes the
八年级物理试卷categories that fall below it… ([4],p 3)
[Ackoff includes a fifth level, “understanding”; typically, that is not done.]
It is suppod that the many and various items of the world have properties that can be obrved. And data is the symbolic reprentation of the obrvable properties ([1] Section 5.2 Defining Data). The prime example of data and data acquisition is provided by automatic instrument systems; an unmanned weather station, for instance, may record daily maximum and minimum temperatures; such recordings are data. Ackoff writes
Data are symbols that reprent properties of objects, events and their environments. They
are products of obrvation. To obrve is to n. The technology of nsing,
instrumentation, is, of cour, highly developed. ([4] p.3)
The acquisition of data can be generalized well beyond automatic instruments. When, for example, a person fills in a form giving their name, address, age, social curity number-- the inscriptions are data. (Actually, the term “raw data” ems apposite.)
Next up the hierarchy is information. This is relevant, or usable, or significant, or meaningful, or procesd, data ([1] Section 5.3 Defining Information). The vision is that of a human asking a question beginning with, perhaps, “who”, “what”, “where”, “when”, or “how many” ([4] p.3); and the data is procesd into an answer to an enquiry. When this happens, the data becomes “information”. Data itlf is of no value until it is transformed into a relevant form. In conquence, the difference between data and information is functional, not structural ([4] p.3).
Information can also be inferred from data-- it does not have to be immediately available. For example, were an enquiry to be “what is the average temperature for July?”; there may be individual temperatures explicitly recorded as data, but perhaps not the average temperature; however, the average temperature can be calculated or inferred from the data about individual temperatures. The
processing of data to produce information often reduces that data (becau, typically, only some of the data is relevant). Ackoff writes Information systems generate, store, retrieve, and process data. In many cas their
processing is statistical or arithmetical. In either ca, information is inferred from data.
同位素分馏
([4] p.3)
Information is relevant data, together with, on occasions, the results of inferences from that relevant data. Information is thus a subt of the data, or a subt of the data augmented by additional items inferred or calculated or refined from that subt.
The next category is knowledge. Urs of this hierarchy often construe knowledge as know-how, or skill, rather than knowledge in the n of the know-that of propositional knowledge. (Some explanation is perhaps in order. Knowledge, in the n of a knowledge ba or knowledge within traditional philosophy, is just a collection of “know-thats” for example, a person might know that the Eiffel Tower is in Paris and know that the Channel Tunnel connects England and France. Additionally, using the different concept, that very same person might know how to ride a bicycle. This latter is a different kind of knowledge, it is skill or “know-how”[7].) Ackoff suggests that know-ho
w allows an agent to promote information to a controlling role-- to transform information into instructions. For example, data as to a room’s temperature might become information when an agent asks “what is the temperature?” and, in turn, that information can become instructions to turn the air conditioner on, if the agent appreciates the way the temperature of the room is controlled-- the information, in this ca, transitions into knowledge-how-to-cool.
Further up the hierarchy comes wisdom – a category which is given only limited discussion in the prent paper. There are two immediate points to be made about wisdom. While wisdom is traditionally taken to be a layer in the hierarchy, few authors discuss it or u it. This may be becau it is not required for the problems they address. (See [1].) Second, criticisms offered in this paper will undermine the
foundational layers, so a successful account of wisdom, whatever nature that might have, will not place it as an upper layer in the DIKW hierarchy.
3.How sound or desirable is this hierarchy?
Centrally, how sound is the view of information?
As a preliminary, let us be clear over the key notion of “truth” (for truth permeates the discussions). What the DIKW theorists u, and what is subscribed to in this paper, is the common-n or objective view of truth. It places contingent truth as a correspondence between statements or beliefs or thoughts or ntences or propositions, and the world [8, 9]. So, for example, the statement that there are crocus in Kew Gardens is true if, indeed, in the world, there are crocus in Kew Gardens. This simple idea is widely accepted: it is for example the one ud in a Court of Law (for instance, to asrt that testimony is true, in a Court, is to asrt that the testimony describes the way things are).
Data in the DIKW theory amounts to, or correspond to, little more than low-level true factual statements (to empirical statements known to be true by obrvation). Automatic instrument systems are the exemplar. So, for example, if there is some data about, say, the daily maximum temperature, it might be reprented, by some statements; namely
Day 1 has maximum temperature 82 degrees.
Day 2 has maximum temperature 80 degrees.
Day 3 has maximum temperature 83 degrees.
and the statements, like all statements, are going to be true or fal. And for “data” in the n ud here, the statements will have to be true. (Inaccurate or mistaken data is not going to be data at all; although, in some particular cas, we might wrongly suppo that it is (See also [8]).)
Then only data, or statements inferred from data, can be information. This theory is esntially conrvative over the nature of information. We are a particular kind of ntient being and view the world through our nsory window. We obrve certain things and not others. There are plenty of occupants of the world that we do not access directly through the window; for example, sub-microscopic particles like atoms, or electromagnetic waves well outside the visible range, like radio transmissions. On the face of it, this view excludes the from being any part of “information”-- they are not among the obrvable data, and they cannot be created by valid inference from the obrvable data. Proponents of this view of information may well feel that to some degree this objection is mitigated by their attraction to instruments. After all instruments extend and enhance our ns; and, for instance, electron microscopes or radio-frequency field detectors provide data about entities not immediately available to us. Maybe so; but that still does not widen the range of data enough. Science tells us that there are obrvable entities and properties, and unobrvable (“theoretical”) entities and properties; and that there are instruments to detect some unobrvable en
tities and some obrvable properties; but also, crucially, that there is a huge domain of the unobrvable for which no instruments of measurement exist. Yet, it is contended here, we should accept that there is information about what to us is the dark world. For DIKW, information has to rest on data (roughly, the outcome of measurements by instrument).
The intellectual backdrop of the DIKW hierarchy is positivism or operationalism, the (now thoroughly discredited) cosmological and methodological viewpoint of the 1930s [10-13]. Refined operationalism has it that concepts that cannot be defined in terms of operations (roughly, measurements by instrument) are meaningless [10, 11, 13]. Historically, the quest here was for certainty. It was thought that if concepts were defined in terms of operations, meanings could be tightly pinned down and further that the statements involving operationally defined concepts could be known for certain to be true or to be fal. Suppodly, under operationalism, we could be absolutely certain of what we mean and absolutely certain as to which statements were true.旺夫是什么意思
There is another worry about DIKW. Universal statements, or statistical generalizations, for example “All forty year old men eat too much” or “Most rattlesnakes are dangerous” also reach beyond the immediately obrvable, becau only some are obrved not all or most. This ems to preclude the generalizations from being information; yet surely “Most rattlesnakes are dangerous” might be
information. Again, DIKW has a respon. Inference from data is possible; this is what happens when, for example, information about the average temperature is produced from individual measurements of temperature. If DIKW adopts the full range of inferences permitted by inductive logic (and, perhaps, statistics or Bayesian statistics) [14, 15], maybe all or most, and similar, statements about the distantly obrvable, but not about the immediately obrvable, could be rehabilitated as information.
But widening the range of permitted inferences caus its own problems. The data that is the inferential ba is intended to be true data. When the only inferences that are permitted from the ba are valid inferences, ie deductively valid inferences, all the conclusions are true also. This means that all the augmented data, and all the information, the functionally promoted data, is also true. So, there is only true data and only true information. But a key property of pure inductive inference is that it is deductively invalid ie it can permit inferences from true premis to fal conclusions [14, 15]. This means that inductively derived data and information can be fal. A proponent of DIKW may or may not be content with this. But either way it is a complete departure from building a pyramid on a solid ba.
The dilemma is: either DIKW does not permit inductive, or similar, inference, in which ca statemen
ts like “Most rattlesnakes are dangerous” cannot be information or DIKW does permit inductive inference is which ca it abandons its core faith that data and information have to be rock solid true.
分散注意力英文Moving on. The conrvatism is displayed in another way. Ackoff’s list of information eking questions-- “who”, “what”, “where”, “when”, or “how many” [4] -- has a notable omission, “why”. Within the DIKW schema, there is a good reason for this. To answer a why-question you have to penetrate beneath the surface, to go beyond the “data”; and that is exactly what the hierarchy approach forbids. Yet it is completely natural for inspectors of an airplane crash, for example, to arch for the information telling why the accident occurred. Many, very many, information eking questions are why-questions. And why-questions typically are going to be answered by a mix of facts and slices from the causal nexus tailored to the context and pragmatics of the question.
Most of the foregoing criticisms can be illustrated by a simple example. The Earth goes around the Sun (as we have learned from Copernicus, Galileo, and others). That the Earth goes around the Sun is information. Yet that the Earth goes around the Sun is not data nor can it be inferred from data; it is not, and could not be, DIKW information. Further, the question of why the Earth goes around the Sun is a perfectly reasonable information eking why-question. And its answer, in terms of initial conditions, gravitational forces, and the like, is itlf information; and the answer, also, would not be
发声练习considered DIKW information.
竖蛋>上海美高双语学校In brief, then, a good account of information should count as information rather more than the DIKW theory permits.
The DIKW theory also ems to encourage uninspired methodology. The view is that data, existing data that has been collected, is promoted to information and that information answers questions. This encourages the mindless and meaningless collection of data in the hope that one day it will be ascend to information-- pre-emptive acquisition. (And this is exactly what happens in some areas of Management Information Systems. For example, there are “data warehous” containing data aimed at encompassing every purcha by each individual customer in every supermarket, cretly recording from the electronic scans at the check-outs, and this data awaits “mining” for information. (My respon is to buy ever more eccentric combinations of goods to defeat the pattern finding algorithms, but that is another story.) There is a rious point to be made. Austin, and his colleagues, have data mined 10 million Ontario patients to “show”, for instance, that Libras fracture their pelvis [16, 17], a conclusion Austin draws is Results from data mining should be treated with skepticism[18]
No doubt many of the results of data mining are perfectly sound, and appraising data mining is not a target of this paper. The asrtion being made here is just that collecting data blind is suspect methodologically.)
