Knowledge Architectures

Structures and Semantics

Denise Bedford

• Publisher: Routledge
• Year: 2020
• Print ISBN: 9780367219437
• eBook ISBN: 9781000286434
• Topic: Computer Science
• Subtopic: Programming Languages
• Index: Computer Science

Knowledge Architectures reviews traditional approaches to managing information and explains why they need to adapt to support 21st-century information management and discovery.

Exploring the rapidly changing environment in which information is being managed and accessed, the book considers how to use knowledge architectures, the basic structures and designs that underlie all of the parts of an effective information system, to best advantage. Drawing on 40 years of work with a variety of organizations, Bedford explains that failure to understand the structure behind any given system can be the difference between an effective solution and a significant and costly failure. Demonstrating that the information user environment has shifted significantly in the past 20 years, the book explains that end users now expect designs and behaviors that are much closer to the way they think, work, and act. Acknowledging how important it is that those responsible for developing an information or knowledge management system understand knowledge structures, the book goes beyond a traditional library science perspective and uses case studies to help translate the abstract and theoretical to the practical and concrete.

Explaining the structures in a simple and intuitive way and providing examples that clearly illustrate the challenges faced by a range of different organizations, Knowledge Architectures is essential reading for those studying and working in library and information science, data science, systems development, database design, and search system architecture and engineering.

SECTION 1
Context and purpose of knowledge architecture

This section makes a case for knowledge architecture. It explains the landscape of knowledge architecture in the knowledge economy. The building blocks of knowledge architecture are laid out, including the essential elements of design, architecture methods, and tools. The second describes a working reference model for knowledge architecture and details three upper knowledge architecture domains. Each upper knowledge architecture domain is broken down into its functional architecture components.

1
MAKING THE CASE FOR KNOWLEDGE ARCHITECTURE

Chapter summary

This chapter explains the role that knowledge plays in the knowledge economy and why it is essential to see knowledge as part of an organization’s stock of capital assets. The chapter discusses the different perspectives of knowledge and highlights the work of intellectual capital researchers in identifying working categories of knowledge assets. The chapter also describes how knowledge assets are different from other forms of capital and how we identify and characterize these different economic properties and behaviors.

Why we care about knowledge

Knowledge is an essential commodity in the knowledge economy. It is a new form of capital. It is the most critical business tool and competitive advantage of any organization. While there has been much talk about managing knowledge over the past 25 years, our treatment and support of knowledge warrants more serious consideration, we must also see and understand knowledge from a design perspective. Because knowledge is complex and dynamic, this means understanding its essential properties and behaviors. Our current architectures are designed to support tangible information objects rather than all types of knowledge.

When we think of knowledge in the broadest and holistic sense, even processes and methods must expand. Most of what we currently define as knowledge or knowledge engineering and management is in factor information engineering and management. It is predicated on the assumptions that knowledge can be better managed when it is tangible and when a decision has been made about the legal or security value of the information. This assumption negates many of the basic properties and behaviors of knowledge and limits the amount we can draw from it. These assumptions restrict our understanding of knowledge – and are the source of many of our fundamental challenges. Our challenge is that we are not working in a vacuum. All of the existing architectures and solutions we have to work with today lock us into working with static and tangible objects. Designing new knowledge architectures provides an opportunity to translate and adapt these solutions to suit the full suite of knowledge. As a foundation for making this mental shift, we need a good grounding in knowledge – all types, and their properties and behaviors.

Knowledge – perspectives and definitions

The challenge we face in talking about and working with knowledge is not the lack of a good working definition. Instead, the problem is that there are many definitions of knowledge, and each of these definitions makes good sense in its original context. There is value in each of these definitions for knowledge architecture (Bontis, 1996, 1998, 2003; Bontis et al., 2000; Bornemann et al., 1999; Brainerd, 1978; Edvinsson & Malone, 1997; Gourlay, 2006; Gruber & Voneche, 1977; Nazari & Herremans, 2007; Nonaka & Takeuchi, 1995; Roos et al., 1997; Saettler, 1990; Zins, 2006). Our challenge in this text is to identify a working definition of knowledge that allows us to design practical and sustainable architectures. Conversely, our working definition must make sense to many different perspectives and work in all these different disciplines. What are some of the disciplines we draw from and must consider in developing our working definition? At a minimum, we must consider philosophy, communications, learning and education, human resource management, business, economics, technology, and information management.

Philosophy addresses knowledge through the study of epistemology. The definition of knowledge in philosophy dates back to Plato. Plato famously defined knowledge as justified true belief (Cornford, 2003) – a core element of the definition adopted by the field of knowledge management. Over the centuries, philosophers have focused on what knowledge is, how it is acquired, and the extent to which individuals can acquire knowledge. In the context of philosophy, knowledge is closely related to truth, belief, justification, intelligence, and wisdom.

Communications treat knowledge as the message or content exchanged between two or more agents to convey or receive meaning. Knowledge is understood to include intent and message, and the processes around knowledge include composition, message encoding and decoding, and message interpretation. In communications, knowledge is defined as a shared system of signs and semiotic rules. Knowledge is the symbolic representation of the sender’s intended meaning. Knowledge is also conveyed or received through observation, imitation, verbal exchange, audio, and video channels.

Learning and education treat knowledge as both a resource and an end state. As a resource, it includes the stock of facts, information, descriptions, or skills associated with an individual. As a process, knowledge is acquired through experience, perception, discovery or learning, storytelling, discovery, teaching, training, or research. In this context, knowledge refers to a theoretical or practical understanding of a subject, area of practice, or discipline. Education is a formal process whose end game is to build knowledge in the individual. Education is achieved through formal institutions and methods, whereas learning and knowledge acquisition take place through real-life experiences. In education, Piaget proposes three types of knowledge: physical, logical-mathematical, and social knowledge. Physical knowledge is knowledge about objects in the world, which can be gained through their perceptual properties. Logical-mathematical knowledge is abstract knowledge that must be invented. Social-arbitrary knowledge is culture-specific knowledge learned from people within one’s culture-group (Driscoll, 1994). We can find all three of these characterizations in common definitions of knowledge from the field of knowledge management. Piaget’s three principles of knowledge development (Piaget, 1976) are represented in Nonaka’s Spiral Model (Nonaka & Takeuchi, 1995; Baumol, 1968), including assimilation, accommodation, and equilibrium. These principles help us to understand the continuous development and essential transitory nature of knowledge.

Business managers and accountants treat knowledge capital as an intangible asset (Carayannis, 2009; Carayannis & Formica, 2008; Carayannis & Sipp, 2005). This perspective compares the tangible and quantifiable attributes of physical and financial capital to the intangible and hidden value of knowledge capital. Business managers and accountants have long recognized the value of human capital – the way they refer to knowledge capital. From this perspective, knowledge capital includes reputation, know-how, and process knowledge – no business process or operation can function without some working knowledge. Business managers also understand the value of knowledge to an organization’s competitive status in a market, to the role it plays in redefining or remaking those markets, and to the composition of those markets. We can already see the impact of businesses that have realized the value and leverage that knowledge capital offers.

Human resource professionals frame knowledge capital as human capital, social capital, or emotional intelligence. Knowledge capital is still an emerging concept in this field – human resource management training has traditionally focused on the management of people as a supporting resource for the business. We manage people through their salaries, their job classifications, skills, and competencies. This perspective is expanding to strategic workforce management and planning.

Economists frame knowledge capital as intellectual capital (Bassi & van Buren, 1999; Baumol & Braunstein, 1977; Bornemann et al., 1999; Edvinsson & Malone, 1997; Goldkuhl & Rostlinger, 2000; Kanchana & Mohan, 2017; Kianto et al., 2017; Prochazkova & Jelinkova, 2014; Roos et al., 1997; Silva et al., 2017; Sveiby, 1997a, 1997b, 2001). There is a high-profile journal focused entirely on intellectual capital – the Journal of Intellectual Capital. Economists treat knowledge as an asset that produces wealth, multiplies the output of physical assets, gains competitive advantage, and enhances the value of other types of capital. Recently, economists have described intellectual capital as a real capital cost because (1) investment in (and replacement of) people is equivalent to or greater than the investment in machines and plants, and (2) expenses incurred in education and training (to maintain the shelf life of intellectual assets) are equivalent to depreciation costs of physical assets.

Technologists and futurists often focus on the role that technology plays in advancing the industrial economy to leverage artificial intelligence, robotics, and the embodiment and use of business rules repositories. While this perspective is essential, it places technology in the dominant role and considers how it impacts human workers.

Information professionals treat knowledge as a form of information. There are many, and many different, characterizations of knowledge in information science. The most significant challenge we face in this context is the interchangeability of two terms – knowledge and information. In this context, information is described as both a thing and a process. Knowledge is assumed to be part of the broader context of information. For some, knowledge is derived from information. For others, knowledge is interchangeable with the term – document – in its broadest characterization. For some, a document is any representation of or encoding of meaning. There is a close alignment of the idea of a generic document and a knowledge object. Here, a document is a fundamental, abstract idea – anything and everything that may be represented or memorialized to serve as evidence. A document can include anything that can be an object of study or understanding. It has some tangible representation that allows us to derive meaning and understanding. However, this characterization is far from commonly accepted in the field of information science. While the fields of information science and knowledge science are closely related, they do not offer a well-developed characterization of knowledge. What we can derive from this field, though, are some basic methods for supporting knowledge availability, accessibility, and consumability. These methods provide a starting point – though not an endpoint – for understanding architecture design.

Our working definition must make sense and be a practical tool we can use to design our knowledge architectures. What can we leverage from across these perspectives? What are the core elements? What is common to all of these perspectives? And, what makes knowledge different from other commodities and resources? From all of these perspectives, we observe that knowledge:

• is both a thing and a process;
• is inherently human;
• is dependent upon context;
• has both intrinsic and conditional value;
• has behaviors and propert...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• List of figures
• List of tables
• Preface
• Acknowledgments
• Section 1 Context and purpose of knowledge architecture
• Section 2 Designing for availability
• Section 3 Designing for accessibility
• Section 4 Functional architectures to support knowledge consumption
• Section 5 Pulling it all together – the big picture knowledge architecture
• Index