When it comes to implementing practical and applicable Knowledge Management solutions in any enterprise, it’s crucial to understand the KM building blocks, known as Knowledge Structures (KSs), that facilitate any and all knowledge activities (e.g. creation, manipulation, movement and destruction). KSs come in big and small sizes and can be arranged in many ways to create new structures that facilitate knowledge work. And, while some KSs are very obvious to most people, many are not. In all cases, KSs are all around us and they all play a major part in all areas of Knowledge Management. Every Data, Information, and Knowledge Management professional should understand what KSs are, how they’re created, and how they can be used. This is especially important if you want to understand how to improve Knowledge Management Maturity for any enterprise.
Synonyms for Knowledge Structures
Just as an FYI, the following terms and phrases are synonyms and can be used interchangeably:
- Knowledge Structure (KS)
- Knowledge Construct (KC)
- Knowledge Artifact (KA)
- Knowledge Container (KC)
- Knowledge Object
- Objectified Knowledge
The Primary Pattern is “Structures of Structures”
Knowledge Structures are simply structures that are composed of many other specifically ordered structures. For example…
- An Essay is composed of specifically ordered Paragraphs.
- Paragraphs are composed of specifically ordered Sentences.
- Sentences are composed of specifically ordered Words.
- Words are composed of specifically ordered Characters and Symbols.
- Characters and Symbols are composed of specifically ordered Pixels.
- Pixels are composed of specifically ordered Colors.
- Etc. (if we wish to get down into quantum physics)
Each structure in the above chain of containment is a meaningful structure, all by itself. Each structure can be contained within another structure. Each structure is composed of one or more other structures.
An interesting aspect of the above example is that, until we get to the structure of Pixels, all structures can be represented, both, physically (e.g. on paper) and virtually (e.g. digitally on a computer screen). Physically, we could have taken a different direction than with Pixels. For example, Characters could be broken down into Ink or Paint, which could further be broken down into Chemical Compounds, which could further be broken down into Periodic Table Elements, etc.
As you can see, this pattern of Structures of Structures (SoSs) is universal and holds true for, both, all virtual and all physical objects because everything in the universe is just a structure that is composed of many other ordered structures. And, while an engine is a structure that exists to move a vehicle or a house is a structure that exists to shelter humans, a Knowledge Structure (KS) is a structure that exists to facilitate one or more knowledge-related actions or activities (e.g. creation, manipulation, discovery, communications, learning, understanding, etc.).
Also, everything that is physical has at least one equivalent virtual representation that allows entities like humans and computers to work with virtual equivalent representations of those physical structures. We see a physical barn and we store a virtual representation of a barn. We see a physical table and we store a virtual representation of that table.
Knowledge Structures support Knowledge Activities
Knowledge Structures are things we create and use in order to facilitate Knowledge Activities that are commonly performed by humans. Examples of these activities include but are not limited to: Thinking, Analyzing, Communicating, Discovering, Learning, and Understanding. Under these higher-level Knowledge Activities are lower-level ones that include but are not limited to:
- Creating and Instantiating,
- Persisting and Accessing,
- Reading and Parsing,
- Calculating (e.g. Adding, Subtracting, Multiplying and Dividing),
- Copying, Cloning, Concatenating and Merging
- Modifying and Changing,
- Communicating (e.g. Transmitting and Receiving),
- Purging and Deleting,
- Etc.
All Knowledge Activities play a key part in how humans work with Data, Information, and Knowledge. As we’ll see, humans also strive to mimic the above functions, which are performed in and by the human mind, in tools like mechanical and digital machines as well.
Knowledge Structures can co-exist in different domain spaces
Somewhere in the evolution of humans, we determined that ideas in our minds could be manifested as physical objects. This process of turning such ideas into physical Knowledge Structures is referred to as the Objectification of Knowledge and the structures that are created by such a process are referred to as Knowledge Objects or Objectified Knowledge.
Interestingly, humans determined that they could create tools that also allow Knowledge Constructs (a.k.a. Objectified Knowledge) to exist in alternate spaces, like on film, on magnetic tape, and in a computer’s memory. Some of these tools, like computers, even evolved to be able to create their own new Knowledge Structures (discussed later in the Algorithms section).
Creation Patterns for Knowledge Structures
There are some very important patterns we use for the creation of Knowledge Structures in different domain spaces. In the case of using three (3) domains that include our minds (Mental), computers and physical space, we see…
- Physical to Mental
- Physical to Mental to Computer
- Physical to Computer
- Physical to Computer to Mental
- Mental to Physical
- Mental to Physical to Computer
- Mental to Computer
- Mental to Computer to Physical
- Computer to Mental
- Computer to Mental to Physical
- Computer to Physical
- Computer to Physical to Mental
- Computer to Computer
Interestingly, all are valid and all are often used.
Knowledge Structures have Traits
All Knowledge Structures are assigned meaningful traits or characteristics. For example, they will have one or more labels, classifications, purposes, and meanings, along with any other traits that may be relevant. And, what makes KSs so interesting is that they are the foundation for all Knowledge Creativity and Innovation and for all forms of Communications. These traits allow us to work with these constructs. For example, we can split, change, group, regroup, order, and contain them in many different ways to create many different new and interesting KSs and then we can ship them around, between people and/or other systems, almost as if they were physical packages.
Knowledge Structures can be virtualized in the Mind and Computers
When we see something physical, we create a virtual representation of what we see in our minds. The recreation may not be perfect as we might not be able to recall every single detail. However, that image represents our collective memories of what we recreated in our minds, allowing us to recall what we know at later times for various activities.
Our minds have the ability to use technologies and tools to recreate many of the images our minds. Every human-created physical object is a manifestation of one or more human ideas that existed as virtual Knowledge Structures before they were ever fabricated as physical objects and that exist as Physical Knowledge Structures. One very special tool humans have created, called the computer, even allows us create virtual representations of many of the KSs we create in our minds.
Very much like creating Knowledge Structures in our own minds, computers give us the ability to create virtual representations of KSs, too. The difference is that instead of those objects existing in our human memories where they can be processed by our minds, they exist in computer memories where they can be processed by hardware running software, very much like alternate co-existing electronic minds that we can use to offload certain types of work to.
With computers we can create and process everything from very simple structures (e.g. characters or arrays) to very complex structures (e.g. 3-dimensional objects that can move and be interacted with). We call these structures Data Structures.
Data Structures are Knowledge Structures
Data Structures (DSs) are Knowledge Structures (KSs) that humans (and computers) create inside computers for consumption and use by computers and humans. Some DSs are meant to exist and be used only in computer space, such as Bits, Bytes, and Object manifestations of Classes. The humans who are capable of understanding them are those like Engineers, Computer Scientists and Mathematicians who work with them at those very low levels. Other DSs are meant to be created, interacted with, and used via User Interfaces (UIs) by humans who have no deeper technical knowledge. Examples include interactive structures like electronic documents (e.g. word-processing documents, spreadsheet tables, and presentations), reports, dashboards, data visualizations and 3-dimensional objects in video games.
Humans invented (and continue to invent) Data Structures to mimic the Knowledge Structures we create in our minds. For example, every element in a physical book, including the entire book itself, can be digitally recreated in a computer environment, such that readers can see the same Table of Contents, Chapters, Index, Paragraphs, Words, Characters, etc.
As mentioned earlier, the term Data Structures is mostly used by professionals like Engineers, Computer Scientists, and Mathematicians. The term Knowledge Structures is most often used by Knowledge Management professionals, Library Management professionals, and User Experience professionals who deal with structures that humans interact with, more directly. However, when it comes to computers all KSs are DSs because, when it comes to existing inside a computer’s memory, a structure must be a Data Structure. There are no exceptions.
Examples of lower-level Data Structures that are typical in computer space include but are not limited to:
- Pixels (such as computer monitor or mobile device screen pixels that can be turned on, colored, and turned off),
- Bits and Bytes that are the foundation for things like Symbols, Characters, and Strings,
- Characters, Numbers, and other non-alphanumeric Symbols,
- Lists, Arrays, Hash Tables (or Hashes), and Trees/Hierarchies,
- Classes and Objects,
- Formulas and Algorithms,
- Buffers, Streams and Files,
- Etc. (The list is long.)
Again, the above DSs are, in fact, KSs but they are rarely referred to as KSs.
Examples of higher-level Data Structures that are more commonly referred to as Knowledge Structures include but are not limited to:
- Master Catalogs and Domain Catalogs with Indexes,
- Entire Digital Libraries,
- Individual Documents (e.g. Web Pages, Word-Processing Documents, Spreadsheets, and Presentations),
- Lists and Tables,
- Charts, Graphs, Reports and Dashboards,
- Interactive Data Visualizations,
- Taxonomies and Ontologies,
- Semantic Relationships,
- Etc. (The list is long.)
Big or small, simple or complex, Data Structures are Knowledge Structures that can co-exist in an alternate virtual reality we call computer space. And, while they are not exactly like those that can exist in our minds, they are close enough to them that we can use them to help with complex processing activities, such as creating other new and interesting Knowledge Structures. An example of this is a paradigm called Data Driven Synthesis that allows us to automatically synthesize Knowledge Constructs like web content and even the automatic synthesis of massive Knowledge Structures called Digital Libraries.
The beauty and power of the above Data Structures is that they are highly reusable and modular tools that can be used for virtual creativity and innovation. We as Data, Information and Knowledge professionals can and often use and reuse them in the construction of other complex Structures-of-Structures. For example, Lists-of-Lists, Linked Lists, Arrays-of-Arrays, Arrays-of-Hashes, Hashes-of-Arrays, Hashes-of-Hashes, Classes that contain any and all of the above as well as other Classes, etc. One of the most advanced Knowledge Structures is that of an Application, which exists as a stand-alone complex structure that other computers and/or humans can interact with.
The possibilities for new and different permutations of Knowledge Structures appear to be endless and it should be noted that they are directly tied to the explosion in global data patterns and trends (like Big Data).
The importance of Algorithms as Knowledge Structures
A very important Knowledge Structure is that of an Algorithm. Algorithms are Knowledge Structures that know how to work with and manipulate other Knowledge Structures, even other Algorithms. In other words, they allow humans and machines to create repeatable formulas or rules that can be used to create, modify and delete other structures. Algorithms can be executed sequentially or in parallel and can even incorporate Feedback Loops that allow them to be self-improving, such as in the example of Machine Learning.
Examples of non-technical Algorithm Knowledge Structures include but are not limited to:
- Check Lists,
- Recipes,
- Directions on medication packages,
- Installation or build Instruction Books,
- How To Videos
- Etc.
Examples of simple computer-related Algorithm Knowledge Structures include but are not limited to:
- Mathematical forumlas,
- If-Then-Else, Case, and Switch statements,
- For Loops, Do Loops, and While Loops,
- Physics Engines,
- Entire software programs,
- Etc.
The truth is that, when it comes to software coding, there are endless possibilities for creating different Algorithms. For example:
- Algorithms for calculating new drug permutations,
- Algorithms for processing insurance claims,
- Algorithms for determining best medical care pathways,
- Algorithms for searching for specific text in endless volumes of text,
- Algorithms for converting text to voice or voice to text,
- Etc.
The power of implementing Algorithms in computer software is that the possible permutations are endless and that we as humans can use the power of computers to execute them, even if the mechanics for doing so are well beyond the reasonable capabilities for humans to do so. Computers will simply implement any and all of them, automatically. This means that the more Algorithms the world comes up with the more knowledge work that can and will be automated.
Interactive Data Visualizations as Knowledge Constructs
Interactive Data Visualizations (IDVs) are another evolving area of Knowledge Structure creation and use. We stress the word interactive because, unlike Static Data Visualizations (SDVs) (e.g. static charts, graphs and non-explorable infographics), IDVs allow humans to interact with them and see new, different, and changing Knowledge Structures, giving them the feeling of being somewhat life-like.
Interactive Data Visualizations are created and made interactive via complex Algorithms like formulas and physics engines (other Knowledge Structures), and their creative permutations are limited only by the creativity in our human minds, and we see many new and powerful
Attribution: Note that the primary image used for this article is a snapshot of the Data Driven Documents (D3js) collage, which can be accessed via the d3js.org web site and which highlights some of the many powerful Interactive Data Visualizations that can be created with software and computers.
An evolving area of practice is the fluid integration of IDVs into Graphical User Interfaces (GUIs) to facilitate knowledge discovery, learning, and understanding. Some examples include…
- Chaining Semantic Node Clusters that allow users to select nodes and see new clusters.
- Filterable Chained Component Views that allow users to filter on specific types of relationships and lead to other chained Component Views.
- Interactive Tree Structures that allow us to dynamically explore parent-child relationships.
Curation of Knowledge Structures
A very powerful outcome of Algorithms is that they allow us to curate Knowledge Structures. Curation includes a large list of activities such as but not limited to:
- Formatting,
- Identifying (e.g. assigning names and identifiers),
- Classifying or Characterizing,
- Organizing (e.g. Grouping and Ordering),
- Cataloging,
- Indexing,
- Prioritizing,
- Etc.,
Curation activities are all further examples of Knowledge Activities and they fall into a very powerful discipline area known as Library Management.
The importance of Library Management Knowledge Structures
Earlier we discussed and provided examples for some critical Knowledge Structures that fall into a very specific Knowledge Management discipline area called Library Management. In short, Library Management is the professional discipline of applying the principles and practices of creating, maintaining, changing, and using Libraries and all their related sub-structures.
Most humans are used to dealing with brick-and-mortar libraries that follow the Dewey Decimal System for classification and leverage Knowledge Structures like Catalogs, Indices, Locations (e.g. Wings, Aisles, Bookcases, and Shelves), Media (e.g. Books, Tapes, Microfiche), Bibliographies, and Tables of Contents. In the constantly evolving digital world, we deal with Digital Libraries that are used to create online virtual knowledge repositories. And, just like physical brick-and-mortar libraries, online virtual libraries and everything contained within them or that are related to them are also Knowledge Structures, even if just virtual.
(Note: Please refer to earlier areas of this article for digital examples of Library Management Knowledge Structures.)
Two examples of different Digital Library types include:
- A Domain-Specific Digital Library that is used to create knowledge repositories which contain subject matter that is specific to a constrained topic topic domain, such as the Knowledge Management Body of Knowledge (KMBOK) that covers KM topics for KM professionals.
- An Enterprise Digital Library that is used to organize and interlink the things that are important to an enterprise (like a company or government agency) so that employees and consultants can find and use things they need to perform their work.
Each follows the pattern of a traditional brick-and-mortar library, using Knowledge Structures like Master Catalogs, Domain-specific Catalogs, Indices, Links to artifacts, etc. However, unlike brick-and-mortar libraries, they have the added advantage of being able to leverage Digital Knowledge Constructs (e.g. Web Pages, HTML Links, Interactive Data Visualizations, Graphical User Interfaces (GUIs), etc.
Summary and Conclusions
Knowledge Structures are those structures we use in our daily knowledge management activities. They can be physical or virtual and exist in multiple domains, such as physical space, mental space, and computer space. There are static knowledge structures and interactive structures. Those that are interactive use other knowledge structures, such as automated formulas and algorithms. Most importantly, Knowledge Structures are the tools for human creativity and innovation and they hold extreme value in our daily personal lives as well as in our professional work lives.
Understanding Knowledge Structures (their types, their traits, how they’re created, how they’re manipulated, and how they’re used is at the very heart of successful Knowledge Management. If you’re a Data, Information, or Knowledge Management Professional (such as a teacher, a practitioner, or a student), Knowledge Structures will be part of your daily lives. Knowing, understanding and using them will be critical to your own successes and to those of the stakeholders you serve.
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