THE NOTION OF “MEANING SYSTEM” AND ITS USE
FOR “INFORMATION SYSTEMS”
Lin Liu and Junkang Feng
Database Research Group, School of Computing, University of the West of Scotland, U.K.
Keywords: Information systems, Information, Meaning, Meaning system, Web search.
Abstract: Mingers (1995) suggests a notion of ‘meaning system’ in order to clarify the relationships between data
(signs), information and meaning, and their bearings on information systems (IS for short). We observe that
there are a few points that need further investigation, which are centred on the basic notions of information
and meaning. In this paper, we summarise seemingly the most influential studies on these two concepts in
the field of information systems. We take a close look at the notion of ‘meaning system’ by drawing on
theories of Dretske (1991) and Devlin (1995) in addition to Mingers (1995). We explore how this notion
may be applied to IS problems by formulating one’s meaning system using an ontology language in order to
improve web searching.
1 INTRODUCTION
In the literature, ‘meaning’ is taken as synonymous
to the semantic content of a concept (Dretske, 1991,
p. 222). Mingers (1995) extends Dretske’s concept
of meaning to include some seemingly strong and
arbitrary features, “meaning is generated from
information by interpreter, carried by sign through a
process of digitalization that abstracts only some of
the information available”. Thus, by ‘meaning’
Mingers also refers to the significances to and the
purposes and intention of a cognitive agent that
perceives a sign/signal. Putting all these together,
Mingers suggests an overarching notion of ‘meaning
system’ within which IS as technological systems is
an integral part.
We observe that the approach embodied by the
notion of ‘meaning system’ is helpful in
understanding the nature of IS and in looking at the
relationship between data (i.e., a type of signs),
information and meaning. We also however believe
that some fundamental concepts should be further
clarified so that the notion of ‘meaning system’ can
be further developed and made applicable to IS
problems. In this paper, we report our work thus far
along this line.
We summarise main viewpoints concerning
‘information’ and ‘meaning’ in Section 2. We give
our view on these notions in Section 3. Then in
Section 4, we look at the relationship between IS
and meaning system. In Section 5, we show how the
notion of meaning system may be applied to the
problem of user profiling so that Web search may be
more relevant to individual users before concluding
the paper in Section 6.
2 CLASIC VIEWS ON THE
NATURE OF INFORMATION
AND MEANING
We are living in an ocean of information.
Information and representations (signs) of
information exist everywhere. Information is
generated at every moment of time. A small object
(sign) is capable of containing and conveying
potentially vast amount of information. Despite of
being such an important element to mankind,
information seem still an ‘explicandum term’
(Floridi, 2005) in academic communities today.
People tend to use the word “information” on a daily
bases without thinking where its concept lie.
Moreover, many believe information is closely
related to computing or intelligent life and cannot
exist without human cognition. In the past decades,
the notion of information was studied by many
leading philosophers in different aspects. The
Mathematical Theory of Communication proposed
52
Feng J. and Liu L.
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DOI: 10.5220/0003268100520059
In Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations (ICISO 2010), page
ISBN: 978-989-8425-26-3
Copyright
c
 2010 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

by Shannon (1949) justifies the statistical attributes
of information. In terms of the semantic aspect,
Dretske’s (1991) Semantic Theory of Information
has a fundamental significance to the study of the
content of information. Barwise and Seligman (1997)
developed the Information Flow Channel Theory
that enables one to identify information flow
between systems with the notion of ‘distributed
systems’.
Despite of those well established theories about
information, the debates around information have
never stopped. Particularly, what is the true nature of
information, and is it possible to give a single and
universally accepted definition to information?
Information has been referred to as processed data,
the propositional content of a sign, data plus
meaning and many more. Moreover, various natures
are being attributed to information including
objective, subjective and combinations of both.
Therefore, finding a clear, justifiable, and applicable
concept of information becomes increasingly vital
for academic researchers and society as a whole.
The study of information can be traced back
many centuries. According to Harper (Lyytinen,
Klein and Hirschheim, 1991), the notion of
“Information” is originally invented in 1387 with the
definition of “act of informing”. It was referred to as
“knowledge communicated” a century later. The
development of modern technology has inevitably
multiplied the number of definitions for information
with varying degrees of complexity. Among them, a
common view is that information is data that has
been processed in some way to make it useful for
decision makers, which is revealed by Lewis’s
(Lewis, 1993) survey of 39 IS texts. Information
embodies an objective nature according to this
assumption, because data is objective and
independent to its observer in term of its existence
and structure. Dretske argues that “Information is
the propositional content of a sign (Dretske, 1991, p.
65), (Mingers, 1995, p. 6)”. The generation of
information is due to reduction in uncertainty of
what might have happened.
Bateson suggests that information is a difference
that makes a difference (Bateson, 2000, p. 286),
which can be interpreted that it is the difference that
generates an event, a sign, a symbol, or an utterance.
Subjectivists Lewis and Checkland believe that
information exists within human’s cognition. As
Lewis argues, “Different observers will generate
different information from the same data since they
have differing values, beliefs, and expectations
(Lewis, 1993)”. Moreover, Checkland formulates
this view as “information equals data plus meaning
(Checkland, 1990, p. 303)”. That is, by attributing
meaning to data, we create information”.
It is hardly surprising to experience such fierce
controversy over the nature of information. Some
philosophers have sensed the powerful, elusive
nature of information and brought out an impartial
idea – the definition of information depends on
different fields of requirements. As Shannon points
out “It is hardly to be expected that a single concept
of information would satisfactorily account for the
numerous possible applications of this general field”
(Shannon, 1993, p. 180). Floridi further emphasises,
“It (information) can be associated with several
explanations, depending on the cluster of
requirements and desiderata that orientate a theory.”
Some philosophers pay their attention to defining
other attributes of information. Shannon is the
founder of the Mathematical Theory of
Communication (Shannon, 1949), which focuses on
the statistical perspective of information. The basic
idea of this theory is that information can be
accurately quantified as long as the unlikeliness, i.e.,
the probability, of the random event is known.
Philosophers and mathematicians such as
Barwise and Seligman (1997) and Devlin (1995)
developed and formulated the Information Flow
Channel theory and the Infon theory. Their
motivating idea is that information flow is made
possible by regularities in distributed systems.
Constraints capture what (information) flows, and
channels reveal why such flow takes place. For
example, a constraint concerning a tree trunk could
be ‘Number of rings’
Ö ‘Age of tree’.
Meaning is most commonly used in the field of
linguistics, e.g., Semantics, although it plays equally
important roles in non-linguistic fields like
Semiotics. The notion of ‘meaning’ may seem
simple, but in reality, the characteristics of the
notion of ‘meaning’ are that it is far too ambiguous
and hard to define. Furthermore, understanding the
relationship correctly between information and
meaning is crucial since this decides how IS and
meaning system are related.
The study of meaning has the same prolonged
history as information. In the past, meaning was
referred to as tenor, gist, drift, trend, purport, sense,
significance, intention, etc. Grice (Grice, 1957, pp.
377-388) divides the convention of meaning into
two categories, natural and non-natural meaning.
The natural meaning is close (if not equivalent) to
the ordinary sense of “information”, for example, a
blown fuse means the circuit has been overloaded,
and that it is raining means that the grass is wet.
Non-natural meaning is relating to language and
THE NOTION OF “MEANING SYSTEM” AND ITS USE FOR “INFORMATION SYSTEMS”
53

semantic studies. In this sense, that it is raining
means that water is dropping down from the sky.
In term of how to define it, Cang and Wang say
“meaning is the link between information and data
(2009, p.2)”, which is concerned with
communication between people that is completed by
the realization of meaning from data to information.
In their view, the meaning of information carried by
data is just a representation and reflection of the
essential integration of objectivity and subjectivity
in people’s lives. It would appear that their notion of
‘meaning’ is concerned with what a piece of
information means to an individual rather than the
literal or conventional meaning of a sign, i.e., what
the sign directly refers to.
As a great epistemologist, Dretske has this
insight on meaning: meaning is the semantic content
of a concept (Dretske, 1991, p. 222). It is the
propositional content of a concept that exhibits the
third order of intentionality. Furthermore, once
formed, a concept has the capability of giving
meaning to its instances. The creation of a concept
in one’s mind involves the development of selective
sensitivity whereby one digitalizes analogue
information carried by stimuli.
In Mingers’ notion of ‘meaning system’, as cited
earlier, “meaning is generated from information by
an interpreter, carried by sign through a process of
digitalization that abstracts only some of the
information available (Mingers, 1991, p.10).
According to him, meaning can be divided into three
levels, i.e., understanding, annotation and intention.
It emphasises on the human agent’s involvement in
producing meaning and its implementations to
mankind.
Devlin proposes the linguistic meaning as a
linkage between utterance type and actual situation
type. “The meaning of an assertive sentence Φ is a
constraint, an abstract link that connects the type of
an utterance of a sentence Φ with the type of the
described situation (Devlin, 1995, p.221)”.
3 OUR ATTEMPT TO CLARIFY
THE NOTIONS OF
INFORMATION AND
MEANING
As aforementioned, due to the elusive and diverse
nature of information, it is extremely hard to find a
completely safe ways of talking about information,
in particularly, an explicit definition covering all
appropriate aspects. Our intention lies on finding a
clear conception of ‘information’.
The nature of information has a significant
impact on how to define it. A piece of information
can be embodied (represented) and carried by a sign
(data are a collection of signs). The sign signifies
something, or rather, it signifies that some event has
occurred. It also has implications for the receiver
(Mingers, 1995). Anything can be a sign as long as it
is ‘signifying something-referring to or standing for
something other than itself’. A sign is an integration
of Representamen (vehicle), Interpretant (sense) and
Object (referent) according to Peirce’s triadic model
(Peirce, 1991). Stamper (1997) constructs an
organisational semiotic framework, which consists
of 6 levels (properties), namely, Physical World,
Empirics, Syntactics, Semantics, Pragmatics and the
Social World.
Sign may be seen within an information context.
Information can be physically carried by a
representamen (i.e., the sign) with some syntactic
property as described in Stamper’s semiotics
framework. The interpretant is implication
(significance) of other objects, which can be seen as
meaning of the sign. This is at the semantics level of
semiotics. For example, a traffic light is a sign. The
information that the sign carries is an instruction to
traffic. When it turns red in a normal circumstance,
for instance, the instruction is ‘to stop’, which is the
meaning of the sign and at the same time one of the
pieces of information that it carries. If the traffic
light turns red in testing, the meaning of it would
still be ‘to stop’, but it does not carry the information
of ‘to stop’ as there is no such instruction to traffic
in the first place.
Despite the connection between sign and
cognitive agents (human beings) in the social world,
despite the abilities of cognitive agents in generating
information through signs, e.g., traffic signs, the
making of the sign is independent of its observer if
any, and after a sign has been made, it is an
objective commodity that exists independently of its
creator as well as its observer if any. Therefore,
information as a constituent of a sign (i.e., what a
sign can tell us truly) is objective, independent of its
carrier (sign) and receiver. It is not created in the
mind of the observer of the sign, e.g., the utterance
of a speaker is out, the information is there no matter
who receives it.
How much and what information is available to
each individual may vary depending on receiver’s
prior knowledge about information source. This is so
called ‘relativization’ (Dretske, 1991, p. 79) of the
information content of a signal, which should not be
ICISO 2010 - International Conference on Informatics and Semiotics in Organisations
54

confused with being arbitrary. Lewis’ argument in
previous section should be refined as different
observers will receive (not generate) different
information from the same data since they have
differing values, beliefs, and expectations.
It may be argued that information can be
produced in a human’s mind due to reduction of
uncertainty occurring in it. For example, a person
stops in front of traffic lights thinking about what he
is having for his dinner. The uncertainty is thus
reduced since he selects one option, e.g., the fish
supper out of other possible choices. However, the
“information” generated in people’s minds is not
within the domain of information that we have
discussed above, which is concerned with the states
of affairs of the real world, and not something in
people’s minds. Moreover, such reduction in
uncertainty is not carried by a sign (e.g., traffic
lights) but by cognitive states.
The above objective characteristic of information
is much clearly taken on-board by the Cambridge
dictionary of philosophy, which defines information
as: "an objective (mind independent) entity. It can be
generated and carried by messages (words,
sentences) or by other products of cognizes
(interpreters). Information can be encoded and
transmitted, but the information would exist
independently of it encoding or transmission."
Information is also measurable as long as the
probability P of a random event is known. Let s
a
be a
state of affairs among a few others of a selection
process S, then Surprisal I(s
a
) - the amount of
information generated at S can be calculated:
I(s
a
)=‐logP(s
a
)
WhereP(s
a
)istheprobabilityofs
a.
Moreover, as an information carrier, a sign has
implication to its receivers (Mingers, 1995), which is
echoed in Dretske’s nuclear sense of information: A
state of affair contains information about X to just
that extent to which a suitable placed observer could
learn something about X by consulting it (Dretske,
1991, p. 45). Information is capable of yielding
knowledge and knowledge requires truth,
information requires it too. This truthfulness is a
necessary condition for DOS (declarative, objective
and semantic) information (Floridi, 2005). Therefore,
mis-information or false-information is not
information, more precisely it is not in our nuclear
sense of information, because they are not true. It
could be ‘negative information’ (ibid.) (i.e., not
information at all) generated due to the equivocation
or noise in a process of information transmission, or
purely the receiver’s mis-understanding. 
Information should not be confused with
meaning. Meaning like other cognitive states, e.g.,
belief, exhibits a third order of intentionality
(Dretske, 1991, p. 173), which means that nested
information that is carried in analogue form is
excluded from the semantic content of a concept. A
concept gives meaning to its instances. For example,
the utterance “Sean is a male adult” does not have
meaning of “Sean’s age is equal to 16 years or over”
or “Sean is not a female”, although they (as
information) are nomically nested in ‘Sean is a male
adult’ if it is contingently true. The production of
meaning involves the digitisation of analogue
information, the creation of a concept and the
instantiation of the concept. Due to length
constraints, we will not discuss the details of this
process here.
Different cognitive systems may abstract
different pieces of information from those that are
carried by a signal depending on its cognitive ability,
e.g., experience, knowledge and understanding. A
broadcast statement “it is snowing” carries a lot of
information. It can be interpreted to be cold by an
elderly man’s cognitive system and he stays in.
Conversely, a boy next door is quite excited to hear
it. He is expecting a snow ball fight and rushes out.
A signal might well have meaning without
carrying any information. For instance, the utterance
“it is raining” has meaning, but carries no
information if it is not true (not raining). It should be
pointed out, although the meaning generated in this
example is not from information carried by “it is
raining”, it still comes from some other sources, e.g.,
mis-information or negative information (Floridi,
2005) mentioned previously, which are not
information at all. That is to say, in this example, the
concept ‘raining’ is mistakenly instantiated possibly
due to mis-information etc.
Unlike information, whose amount may be
measured as said earlier, meaning is not measurable.
It cannot be measured by the probability of an event.
“it snows in July” does not have more quantity of
meaning than “it snows in December”, even though
the former carries a larger amount of information
than the latter as the probability of the former is far
lower than that of the latter.
4 MEANING SYSTEM AND IS
By ‘meaning system’ therefore we refer to a humans’
epistemological system based on perception and
THE NOTION OF “MEANING SYSTEM” AND ITS USE FOR “INFORMATION SYSTEMS”
55

cognition from which meaning is produced through
interacting with the real world, which involves
digitalising information from those carried by signs.
Therefore, information is imperceptible directly to
human agents, that is, humans can only interact with
information through their meaning systems. In other
words, information cannot be used by a human agent
until it connects to their meaning systems within
which human beings operate.
This notion of ‘meaning system’ is built upon
Mingers (1995) and extends Dretske’s notion of the
‘semantic content’ of a concept to three levels.
1. Understanding, the primary or literal
meaning of a sign. This level of meaning is
commonly shared by all competent cognitive
agents of a community, e.g., what a sentence
refers to directly. This is because they invoke the
same concepts and the instantiation process can
hardly go wrong. Such meaning is embedded in
the signal (the sentence in the above example),
thus it has objective features in that the agent
does not contribute anything to it.
2. Connotation, secondary meaning. This
extends the initial meaning of the sign to include
nested consequences known and available to a
receiver. This level of meaning is inter-
subjective, which is captured by a group of
people who share the same cultural background
and language. But different groups of people
may obtain entirely different connotation from a
given sign.
3. Intention, the third and individual meaning,
which is realised by a particular person based on
his own personal experiences, feelings and
motivations at a particular time. As a result,
appropriate action is taken, likes above “It is
snowing” example. Therefore, this level of
meaning is subjective.
Hence, the notion of ‘meaning system’
incorporates the importance of human interaction in
meaning generation, which would be relevant to
information systems. As aforementioned,
information is capable of yielding knowledge
(Dretske, 1991, p. 85) from which the observers (e.g.,
human beings) can learn something about certain
state of affairs in the real world. In information
systems, information held in analogue form can be
processed through information processing machines,
e.g., computers. This information is then continually
processed and interpreted into meaning (in the sense
just defined) through human’s meaning system.
Information systems are ultimately designed to
serve mankind. Traditional IS implementations are
concerned very little with individual requirements;
they treat users as a whole group. We observe that
information systems should adopt the notion of
‘meaning system’ in developing user-oriented
applications, e.g., web searching, online shopping,
digital libraries, and so on, and modern technologies
should facilitate it by providing useful mechanisms.
5 APPLYING THE NOTION OF
‘MEANING SYSTEM’ TO WEB
SEARCH
We suggest that the notion of ‘meaning system’
have the potential of a wide spread application
across disciplines. We now take a look at web
search as an example to demonstrate the concept’s
significance in the IS field. Unlike traditional search
engines, e.g., Google, Yahoo, Sohu, which has been
designed to work with natural languages, web search
can adopt user profiling to help achieve more
accurate, efficient web searching by semantically
matching information in the web and the user profile,
and we suggest that the latter can be captured and
formulated with the notion of meaning system for
individual users.
The material to be presented here is based on
Reda (Rada, 2010).The approach is based on user
profiling with ‘meaning system’, which enables
personalizing each web search, which includes
queries being answered according to each user’s
profile. For instance, by typing “IT”, the search
engine will produce different URLs for each
individual user (or group) such as a computing
student, a scientist, or an NHS nurse by following
their respective meaning system.
In our experimentation, such a user profiling
makes use of our notion of ‘meaning system’. The
three levels of it extend the search results reflecting
the personalized search requirements. We
summarize the procedure and strategy of our system
for web search that makes use of the notion of
‘meaning system’ below:
1. After the query has been made by the web
search user, the web search system goes to the
user profile and tries to find the meaning for the
query (e.g., the term “information”).
2. In order to make the search more specific and to
find the best results, the search system will find
out what is meant by the term ”information” for
This specific web user by citing his profile,
ICISO 2010 - International Conference on Informatics and Semiotics in Organisations
56

Figure 1: Web search system using the notion of ‘meaning system’ idea (Reda, 2010).
which has been previously stored in the system.
3. Appropriate search results—URLs are selected
and brought forward. Those URLs are directly
linked to the primary meaning of the query
under this particular user profile. For instance,
for the search results on “information”, the
relevant URLs will be quite different for IT
professionals, philosophers, lawyers, doctors,
and so on.
We use ontology to capture and formulate user
profiles. Below is a sample user profile written in
the OWL ontology language, which captures a class
called “Searcher” and attributes such as “username”,
“occupation”, “age” and “gender”. This profile is an
integral part of our web search system.
This user profile ontology below is written in the
OWL language, which captures a top class called
“Person”. This profile is an integral part of our web
search system.
<owl:Classrdf:ID=”Person”>
<owl:DatatypePropertyrdf:ID=”name”>
<rdfs:domainrdf:resource=”Person”/>
<rdfs:rangerdf:resource=”&xsd;string”/>
</owl:DatatypeProperty>
<owl:DatatypePropertyrdf:ID=”dateofbirth”>
<rdfs:domainrdf:resource=”Person”/>
 <rdfs:rangerdf:resource=”&xsd;integer”/>
</owl:DatatypeProperty>
<owl:DatatypePropertyrdf:ID=”gender”>
<rdfs:domainrdf:resource=”Person”/>
 <rdfs:rangerdf:resource=”&xsd;string”/>
</owl:DatatypeProperty>
</owl:Class>
<owl:Classrdf:ID=”Education”>
<rdfs:subClassOfrdf:resource=”Person”/>
<owl:DatatypePropertyrdf:ID=”degree”>
<rdfs:domainrdf:resource=”Education”/>

 <rdfs:rangerdf:resource=”&xsd;string”/>
</owl:DatatypeProperty>
<owl:DatatypePropertyrdf:ID=”level”>
<rdfs:domainrdf:resource=”Education”/>
<rdfs:rangerdf:resource=”&xsd;integer”/ >
</owl:DatatypeProperty>
</owl:Class>
<owl:Classrdf:ID=”Profession”>
<rdfs:subClassOfrdf:resource=”Person”/>
<owl:DatatypePropertyrdf:ID=”Occupation”>
<rdfs:domainrdf:resource=”Profession”/>
 <rdfs:rangerdf:resource=”&xsd;string”/>
</owl:DatatypeProperty>
</owl:Class>
<owl:Classrdf:ID=”Experti se”>
<rdfs:subClassOfrdf:resource=”Person”/>
<owl:DatatypePropertyrdf:ID=”skill”>
<rdfs:domainrdf:resource=”Expertise”/>
 <rdfs:rangerdf:resource=”&xsd;string”/>

Create Tables
in Access
Mean1 (URL)
Mean2 (URL)
Mean3 (URL)
For each User
Map
user’s
Profile
Checked
for
Meaning
of
“
Informati
on”
Query
“
Information”
Map
Query
search
engine
(Search
for
specific
meaning
of
“
Informati
on”
Find the
best
relevant
URL
Find
more
relevant
URL
(To give
the user
more
options)
(Ontology
Base)
User’s
Minger’s
Meaning
System
Create Forms
in Access
Mean1 (URL)
Mean 2(URL)
Mean3 (URL)
For each User
THE NOTION OF “MEANING SYSTEM” AND ITS USE FOR “INFORMATION SYSTEMS”
57

</owl:DatatypeProperty>
<owl:DatatypePropertyrdf:ID=”depth”>
<rdfs:domainrdf:resource=”Expertise”/>
 <rdfs:rangerdf:resource=”&xsd;string”/>
 </owl:DatatypeProperty>
</owl:Class>
<owl:Classrdf:ID=”Interest”>
<rdfs:subClassOfrdf:resource=”Person”/>
<owl:DatatypePropertyrdf:ID=”business”>
<rdfs:domainrdf:resource=”Interest”/>
 <rdfs:rangerdf:resource=”&xsd;string”/>
</owl:DatatypeProperty>
<owl:DatatypePropertyrdf:ID=”sports”>
<rdfs:domainrdf:resource=”Interest”/>
 <rdfs:rangerdf:resource=”&xsd;string”/>
</owl:DatatypeProperty>
<owl:DatatypePropertyrdf:ID=”others”>
<rdfs:domainrdf:resource=”Interest”/>
 <rdfs:rangerdf:resource=”&xsd;string”/>
</owl:DatatypeProperty>
</owl:Class>
6 CONCLUSIONS
In this paper we have taken a look at the notion of
‘meaning system’, and we explored how it might be
clarified and extended. We also looked at how it
might be applicable through an experimentation of a
Web search system. To this end, we have provided
an analysis of two fundamental but controversial
elements: “information” and “meaning”. We
subscribe to the viewpoint that information is an
objective commodity. It exists independently of the
carrier, or receiver, if any, although the quantity and
quality of information available to each receiver may
vary depending on their background knowledge
about information source. But this relativization of
information should not be seen as evidence that
information per se is subjective, and it is only a
matter of how the same information source is looked
at. The creation of meaning involves concepts and
instantiations of concepts. Meaning ultimately
comes from the semantic content of a concept,
which is an agent’s cognitive state. Meaning on
different levels can be objective, inter-subjective or
subjective. This is because a concept gives meaning
to its instances through instantiation, and
instantiation could be subjective, arbitrary and
mistaken, and no information has to be involved in it.
When a set of information is involved, different
agents may digitize the same set of information by
invoking different concepts, thus different meaning
is generated.
Moreover, humans have to rely on their
individual meaning systems (that is, their system of
concepts) to get access to information, which can be
carried by its potentially multiple representations.
Once accessed, information becomes useful to serve
the mankind or has impact on them. The notion of
‘meaning system’ seems useful in designing IS
applications. It provides us with a way to convert
hard and technology-oriented IS into soft and user-
oriented ones. Our experimentation with Web search
seems to have given preliminary evidence to support
such a hypothesis.
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