Upon completion of this lesson, you will be able to:
Information science is the study of information and its structure, the utilization of information, its governance, and its valuation. Central to its study is knowing what information is. This lesson explores the conceptual nature of information and the basic principles that guide the study of information: a philosophy of information.
The term philosophy of information was first coined in the 1990’s by the prominent information scientist and philosopher Luciano Floridi (Floridi, 2013). Floridi has published numerous books and papers on this subject with the intent of defining a coherent conceptual framework of information. In this lesson, we will utilize on Floridi’s basic principles and present a framework of information for the design of information architectures and digital ontologies.
The term information has been defined over the decades in numerous ways, many of which are incomplete, lacking, or narrow. In fact, the term “information” is often used so metaphorically and in such abstract ways that its actual semantics is frequently left unclear.
Floridi (2013) proposes four perspectives of “information”:
From this, we argue that information is “knowing” something and “doing” something.
We propose that information can be looked at as objects of two flavors: data objects and process objects. Both kinds of objects have associated constraints and are subject to rules. The objects can be composed into aggregate or composite objects. Every object has one or more attributes and one or more properties. The structure of the object and its attributes are defined by the class to which the object belongs. Every object belongs to exactly one class.
Information objects are conceptual in nature and are representations of a physical or abstract object in the perceived world. However, when information objects must be stored or processed in an information system, their representation must be projected from the perceived realm into a digital realm. It is a projection as only a subset of all attributes are generally required in an information system. For example, the data object Customer: {Name: ‘Judy Garfield’, Age: 52, …} has an infinite set of attributes of which only a subset is necessary to be tracked in an information system. So, the perceived data object’s attributes are projected to a lower dimensional space in the digital realm. The attributes of an information object can be viewed as an object’s dimensions.
Let’s consider an example. The table below is a tabular presentation of some of the attributes of data objects belonging to the class Customer. Obviously, many attributes are not useful when we process or store the objects in an information system. Naturally, an important question would be: which attributes are important and relevant? This requires creating boundaries for the digital realm, often by defining use cases that elaborate on the utilization of the objects within processes.
df.c <- data.frame(name = c('Dunham, Jeff','Pfalz, Maria'),
birth.date = c('',''),
haircolor = c('',''),
credit.score = c('',''),
gender = c('',''),
weight = c('',''),
blood.type = c('',''))
print(df.c)
## name birth.date haircolor credit.score gender weight blood.type
## 1 Dunham, Jeff
## 2 Pfalz, Maria
Information objects must often be used by humans or exchanged between information systems, so the object’s attributes must be rendered onto a medium. A common medium is a table. A rendering onto a medium is also a projection as not all attributes of an object need to rendered.
Bawden, D., & Robinson, L. (2016). “A different kind of knowing”: speculations on understanding in light of the Philosophy of Information.
Floridi, L. (2013). The philosophy of information. Oxford: Oxford University Press.
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