Semantic Databases: An Information Flow (IF) and Formal Concept Analysis (FCA) Reinforced Information Bearing Capability (IBC) Model

economickiteInternet and Web Development

Oct 21, 2013 (3 years and 9 months ago)

93 views

Computing and Information Systems


©
University of Paisley 2006

1

Semantic Databases: An Information Flow (IF) and Formal
Concept Analysis (FCA)
Reinforced

Information Bearing
Capability (IBC) Model

Yang Wang and Junkang Feng

Database Research Group



Semantic Database (SDB) seems hitherto somehow
overlooked

in the lit
erature compared with its

big
brother

, Semantic Web.
W
hat are the
hindrance
s to
the
development

of SDB, which hence have to be taken
into
account

as we observe, include
information
representation, knowledge management,

meaning
elicitation, constraints/re
gularity identification and
formulation, and also partiality
preservation
. We
propose an
architecture
, which is a result of
reinforc
ing the notion of the Information Bearing
Capability (IBC) that we put forward elsewhere before
by applying the
theory

of In
formation Flow (IF) and
that of Formal Concept Analysis (FCA). We believe
that this
architecture should

enable

SDB to cover a
number of these
aspects
, which build upon and go
beyond the relational database (RDB).


1. INTRODUCTION

Semantic Web

(SW)
is the

supreme
elegance

of

topic
s
, which

covers
numerous

fields, such as
knowledge
organization

and
management
, network
technology and even data modeling.
C
omparing to this
prosperous

triumph
, the
seemingly

evident lack of
attention to Semantic Database (SDB) wo
uld appear
rather
peculiar
.
Whereas

it is well
know
n that SDB
aims at capturing, modeling and
yielding

meanings
rather than raw data, we observe that the short of
robust theoretical
modeling

foundation and
guidance

lies

as a gulf before the

fortune

.

In
our opinion, if
we want to achieve a satisfactory SDB, not only
primary pre
-
requisites
such as capturing

more
semantics and constraints, but also profound concepts
of information, representations and partiality, need to
be
addressed
.

To get
across

this gu
lf, the foundation of this research
is a series of theories (we refer to them
as ‘SIT’
, short
for Semantic Information Theories) concerning
semantic information and information flow including
Dreske

(1981), Devlin (1991), and
in
particular
,
Barwise and Sel
igman

s (1991) information channel
theory

(
IF
for short)
.
We believe that an Information
Flow (hereafter
IF
for short)

and

Formal C
o
ncept
Analysis (FCA) reinforced Information Bearing
Capability (IBC) model (We will say more about it
shortly) provides a ne
w prospective to SDB, which
both

assure
s traditional requirements of design
and
brings up some
philosophical

and
mathematical

insights. This would, therefore, promote SDB to be
compatible with
Knowledge

base (KB) and hence to
be a strong support for SW.

1
.1
A Short Review of
Semantic

Databases (SDB)

A database system is a representation
system, which

should be able to reflect real objects in the
circumstance being modeled.
T
he content of a
database rests with what actually exists in the modeled
domain whil
e any
change operates

on this content
should
correspond with

what happens to those real
world objects.
S
ustaining this tie is not easy as at the
first
glance
.
D
esigning a data model that captures as
much as meaning as the modeled domain is the
solution of
many researcher
s (Hammer
and
McLeod

1981, Jagannathan et
.al, 1988, Tsur and Zamolo
1984).
T
o this end, concepts around SDB came into
the scene.

Bearing the goal

of representing, describing and
structuring more semantics and meanings than
contemporary

database (viz.
Relational

Database) in
mind, SDB needs to be closely related to the modeled
domain. Hammer addresses a number of
criteria that

should be enforced dur
ing SDM
design

(
Hammer
and
McLeod

1981)
:



The constructs of the database model should
provide for the explicit specification of a large
portion of the meaning of
a database. So
called semantic expressiveness is not
sufficiently achieved by many current data
modeling techniques, such as hierarchical,
network, and relational models



A database model must support a relativist
view of the meaning of a database, and allo
w
the structure of a database to support
alternative ways of looking at the same
information.

Being capable of capturing more
meaning requires never rigid definitions and
distinctions between

entities

,

attributes


and

association

.


2



A database model mu
st support the definition
of schemata that are based on abstract entities.
This point, in fact,
addresses
that a database
should have the
mechanism

to support
possible semantic
constraints
.

In the related literature, there are mainly two most
interesting
streams identified by the authors in SDB
modeling. The first one is that some of the researchers
are developing their SDM
structure

on the root of
available modeling techniques. Most related to this
research, some
system
s are inheriting the basic
modeling
constructs of RDM

s apparatus, for
example, Iris Data Model (Lyngback and Vianu
1987), Generic SDM (Chen and McLeod 1989) and
SDB
management

System SIM (Boyed 2003).
Meanwhile, Rishe and his group build up a Semantic
Wrapper over RDB which produces set of
SDB tools
including
Knowledge

database tool, Knowledge base
and Query
Translator

(
http://n1.cs.fiu.edu/SemanticWrapper.ppt
). The
second is that some research shows that SDB is more
likely linked to On
tology and Knowledge base
(
http://www.fmridc.org/f/fmridc/dmt/sdm.html
). This
would seem to
orientate

SDB to
flourishing

the
development of SW.

Besides this, currently, research around SDB
encount
ers numerous obstacles. The bottleneck, as we
have identified, resides in lack of certain infrastructure
to retrieve semantics and formulate semantic
constraints, not from traditional database point of view
but follow vigorous guidance of Semantic Informat
ion
Theory (SIT in short). We believe that by
philosophically separating truly information from raw
data, dually grasping semantic constraints and
partially representing semantic information relation,
an advance model of SDB can be achieved.

1.2
IF

and
FCA

Based IBC Prospect of SDB

In 1998,
we
identified a research problem, namely the
‘information content’ of a formalized information
system

(Feng 1998). In that paper numerous works
were cited and it was shown that the main cause of
this problem seemed that
information had been treated
as ‘mystical liquid’.
We

then argued that the lack of
clearly expressed and defined ‘information content’ of
a conceptual data schema was responsible for many
difficulties in data modeling and analysis as a process
of inquiry,
which is a basis for the design of an
information system.

Then in 1999 we formulated a notion called

information bearing capability


(IBC for short) by
drawing
on
interdisciplinary views of information
creation and transmission (Feng 1999). A four
-
facet
principle currently elaborates this notion, which is
concerned with a set of sufficient and necessary
conditions for the IBC of an information system. The
conditions are:
information content containment
,
distinguishability
,
accessibility

and
derivability

(
Feng
2005). The principle about IBC and their associated
concepts that have been put forward in a series of
research papers (such as Xu and Feng 2002, Feng and
Hu 2002, Xu 2005, and Wang and Feng 2005a) may
be seen as
forming
an innovative perspective for
looking at information systems.

N
o
w, IBC as a
cornerstone is applied to a number of research
problems that are being looked at by our group such as
schema mapping, data exchanging and modeling. The
ideas around IBC however should be further
developed and t
ested in real world applications. To
this end, it seems that the most appropriate tool to
reason about and verify IBC would be IF combined
with FCA. We envisage that
endeavor

along this line
will
uplift the articulation
of what might be called
‘the
microsc
opic infrastructure’ of the IBC

principle to an
adaptable, adoptable and applicable level in SD
B
modeling
.

This paper proceeds as follows.

In the next section, we
highlight some
aspects

of
SDB modeling
that seem to
have been overlooked
in the light of SIT

rooted IBC
model. Our approach of combined use of IF and FCA
in the IBC model, which would,
we believe
,

advance
the state of the art of
SDB, is introduced in section 3.
Following this, a conceptual picture of IF and FCA
reinforced IBC model for SDB descri
bed and
elucidated in section 4.

2. WHAT SHOULD A SDB

MODEL,
REPRESENT AND PROVID
E?

As aforementioned, SDB is proposed in the literature
to address those problems encountered in other forms
of data modeling. As summarized by Boyed (2003),
there are severa
l essential goals, which need to be
sustained, during SDB development. The SD
B

is a
high
-
level semantics
-
based database description and
structural formalism for databases (Hammer, 1981).
Although attempting to capture all the semantics of
the modeled domai
n is unattainable, SDB should
endeavor to incorporate most of the semantics. SDB
advances RDB and other database models in terms of
its real
-
world perception of the problems, different
perspectives of queries, and most importantly its
inheritance
-
based hie
rarchical modeling structure. In
addition to these known characteristics, following the
insight of IBC based on SIT, we would propose more
significant features for SDB. Only when these features
are delivered can we say that SDB is satisfiably
achieved.

2
.1 Data, Information and Semantics

Database is the vehicle for storing and providing
information. Without

the
guidance

of
interdisciplinary


3

philosophical
semantic information

theory
, it is not
surprising that contemporary database modeling dose
not separa
te data and truly information.
Notwithstanding modeling methods like RDB being
many and varied,
as far as

SDB

is concerned
, it should
broaden its edge to tackle the truth of data,
information, meaning and semantics
in order to
captur
e

semantics

and
to
solv
e

some

difficult

issues,
for example, query answering, lossless
transformation
,
etc.

I
n a typical
contemporary

database,

what you see is
what you get


is the
prevailing

feature.
R
elation
between data and information remain
s

scrupulously

bypassed.
F
or a lo
ng decade, data with its meaning is
treated as information in the context of database
(Checkland, 1981). A famous schema transformation
approach, i.e.,

information capacity


(IC) (Miller
1993),
straightly

takes data instances of schemata as
information. F
using
Organizational

Semiotics (OS)
into database,

meaning is created from the
information

carried

by signs


(Mingers 1995).

A

veritably

practical

SDB should
take

the
challenges
that lie

in several aspects around definitions of
information, information c
ontent and meaning.
Some

of my
colleagues

have provided

an analysis about this

(Wang and Feng 2005). Firstly, instances are not
always faithful to their semantic types. Traditionally,
the
schema of
a
database is
thought

to represent the
type level of infor
mation while database instances fill
into these type
level
classes
whereby receive

their
semantic
s

or meaning

from the classes
. However, this
view
overlooks the

facts that
instances may
not loyal
to
their

respective

semantic

infrastructure
s
.
T
hese
instance
s
do

not
represent

any information
that
originated the types (
Dretske 1991).
S
econdly, the
meaning of data in the database is not
necessarily

to
be

part of their

information

content
. SDB should be
able to use alternative ways to represent the same
informat
ion.
T
herefore, a data construct
represents

a
piece of information
only
when the information
content

of the data
construct includes

that piece of
information.
It is not convincing to use meaning as the
criteria for the information content of a piece of dat
a.
F
inally, it is not
adequate

to take the ability of
accommodating

instances into the schema as the
information capacity of data constructs in the database
(Wang and Feng 2005).
The f
ewer constraints being
modeled,
the
less specific
the
instances are. Hen
ce,
less

information there
is
. SDB modeling
should

take

this point into
consideration
and facilitate it
.

2.2 Constraints and Representations

No matter what form it is in; a database is after all
need to represent objects and relations in the
represented
d
omain. The modes of representation
(Shimojima
1996
) obey structural constraints that
mirror the regularities that govern things going on in
the represented domain. Any representation involves
certain kind of information flow. Information flow
results from
the regularities in a distributed system
(Barwise and Seligman 1997, P.8).

Contemporary database like RDB limit themselves
into a particular structure of constraints such as
relational objects and associated relations. SDB should
go beyond these limits in

the way of finding the best
fit between the representing system and the
represented domain. Apart from this aspect, SDB
should also
ensure

that its reasoning be consistent with
the represented domain. In other words, reasoning
over constraints needs great

care. Wobcke (200
0
)
identifies the differences between schema
-
based and
information flow based reasoning. The former is
partly subjective and defeatable contrasting to the
objectiveness and non
-
defeatability holding by the
latter. If given a fixed context

by discarding all
alternative situations, schema
-
based reasoning and
information flow based reasoning are transferable.
Shimojima (1996) uses basic mathematical
instruments to model constraints in order to perform a
rigorous investigation on a wide range
representation
issues. His research provides a sound theoretical
foundation for developing our IBC model for SDB in
virtue of inferential reasoning intimate to what
happens in the domain to be modeled.

2.3 Partiality

Talking about semantics, it is evident

to many
researchers,
especially

those
who
are

familiar with
logics and
linguisti
cs, that there are

holes in reality


(Duzi 2003).
T
hese holes resid
e

in our abstract way of
modeling particular dependency relations among real
world objects.
M
any
attempts

h
ave

been made to
philosophically address such issues
as

Possible World
Semantics and Situation Semantics.
A
s
aforementioned
, in database, there exit
instances that

do

not
inherit

semantics from its
corresponding

class
types. Following Duzi (2003), if we ta
ke these
instances

as the logical
construction

C (not unlike
the
notion of ‘
concept


of Dretske 1981) for the

mode of
representation

, which is discussed in previous
section, it should link the expression E
and its
denotation D
.


Problem
s

arise when we
use empty concepts, the
construction C
will

fail to
achieve

anything, not even
any meaning
. A
s a result, the denotation D
will

fail to
give any truth
-
value
in

an
argument.


4

M
acroscopically
, it is necessary for SDB to be
equipped

with partial order to handl
e
overall

informational relationship
s
. Based on Dretske

s
information flow
(1991)
and Barwise and Perry

s
situation semantics

(1993)
, Wobcke (2000) argues that
using conditionals as basic
appliance
, people could
evaluate the
subjectiveness

and intentionali
ty of a
collection of schemata. The idea is
to
treat those
conditionals as
expressing

constraints which are
actually
informational relations between facts and
events of the kind that can be modeled using structures
of situations (Wobcke 2000).
T
he order of

situations
for the collection of constraints is in
the
form of
partial order supporting subjective reasoning.
I
n
certain circumstances, i.e., providing certain fixed
context (situation), reasoning on this order is identical
to the reasoning of information

flow.
A
lso, in Duzi

s
thesis (2001), she point
s

out that information content
inclusion relations (
in relation

to attributes) are of
partial order.
M
ost specifically, she
formalizes

informational capability in
a
complete lattice based on
the power set of
t
he attributes

in question
.
F
urthermore
, it is interesting that this lattice is proved
to be isomorphic to its substituting partial ordered set
of

equivalence

classes.

Therefore
, for the sake of manipulating informational
scenarios
, the need of supporting p
artial order of
the
IBC model both philosophically and
mathematical
ly
should not be
ignored
.
M
oreover,
we believe

that such
a work would be aligned with issues in
knowledge

representation in
the
AI field.

3. ARCHITECTURE BASE
D UPON
INFORMATION FLOW (IF
) AN
D FORMAL
CONCEPT ANALYSIS (FC
A)

The central idea of IBC
is

called the IBC principle.
This principle, i
s made up of

conditions of
information
content
containment
,
distinguishability
,
accessibility

and
derivability

and it is

put forward

by Feng
(2005)
and hi
s
colleagues

through a period of
arduous work

in the sense of drawing interdisciplinary views of
information creation and transmission

(Feng 1999,
Xu
and Feng 2002, Feng and Hu 2002, Xu 2005, and
Wang and Feng 2005a
). IF is first introduced into IBC
for

re
asoning about and
for
verify
ing

the principle
(Wang and Feng 2005).
A
s being successively
compatible

and content with
the

IBC, IF
has

become a
headstone

for further
development

and application of
the IBC model.
For the
purpose of
elevat
ing
implementation
,
FCA is probed
and found that it is

adaptable,
applicable

and
adoptable

both theoretically
and practically with IF.

3.1
Channel
-
Theoretical

Information Flow

The Channel
-
theoretical Information Flow theory (IF)
is a mathematical model of semantic informatio
n flow.
Information flow is possible due to the
regularities
among
normally disparate

components of
a
distributed
system.
I
t is known that such a
theory

succeeds

in
capturing

partial order
of classifications (Kalfoglou
and Schorlemmer 2005) that
underl
ies
the
flow of
information
.

Sophisticated
notions (we do not go into
detail
s

here)

stemming from IF
now
have been
formulated for explorations on semantic information
and knowledge mapping

and exchanging. Kent
(2002a, 2002b) exploits semantic integration of
on
tologies by extending a first order logic based
approach (Kent 2000) which is also based on IF. An
information flow framework (IFF) has been advocated
as a meta
-
level framework for organising the
information that appears in digital libraries, distributed
d
atabases and ontologies (Kent 2001).
From

Kent’s
work, Kalfoglou and Schorlemmer (2003a) develop an
automated ontology mapping method in the field of
knowledge sharing

and
cooperation. IF and its
surrounding concepts are
also
relevant

to
solving

problems o
f semantic interoperability (Kalfoglou and
Schorlemmer 2003b). A
part from this main stream of
applications,
IF
supports various
research efforts

from
defensible

reasoning (Cavedon 1998)
;

endo
-
perspective formal model (Gunji et al 2004) to
semiconcept and p
rotoconcept graphs (Malik 2004).

Besides

the effective effort of using IF to represent,
capture and model constraints for a given
modelled

domain, it is

also observed that IF ‘was
not
developed
as a tool to be used in real world reasoning’ (Devlin
1999) a
nd we observe

that it is on its own
insufficient
for
describing

domain information or knowledge. To
fill these gaps, Formal C
o
ncept Analysis (FCA)
was
proposed as a silver bullet.

3.2
Formal Concept Analysis (FCA)

FCA
was

developed by Rudolf Wille (Wille 1
982) as
a method for data analysis, information management,
and knowledge representation (Priss
2005
a).
Presumably due to
its applicable

nature
, i
t does not
take long for FCA to become a
common

interest in
many
research

communities, for example, social net

work analysis (Freean and W
h
ite 1993), linguistics
(Priss
2005b
), and software engineering (Fischer
1998
,
Eisenbarth et al. 2001).
A
s
aforementioned
,
FCA provide
s

solid foundations

for not only
information and
knowledge

retrieval

by its
underlying

mathema
tical theory (Godin et al. 1989, Kalfoglou et
al. 2004) but also
for
respective representations by
concept lattice (Wille 1982, 1992
, 1997b
) along with
concept graphs (Prediger and Wille 1999)
. We
maintain that the use of FCA will supplement with
IF
in SDB

modeling.

By using IF along
,
it would appear

that t
he
construction of an ‘information channel’ in many

5

cases is difficult when applying IF to real information
system problems. To alleviate it, we envisage that
‘Conceptual Scaling’ techniques (Ganter and W
ille
1989, Prediger and Stumme 1999)’, which are affinity
with FCA, will be useful. Furthermore, reasoning and
inference over difference levels of a channel can be
characterized by ‘Concept Graph’ (Prediger and Wille
1999) in the light of FCA
-
based ‘Concep
t Lattice’
(Wille 1982, Wille 1992, Wille 1997
b
). In other
words, FCA provides
the

investigation with a basis for
extraction, representation and demonstration of
informational
aspect

of semantics
, and at the same
time IF
-
based techniques/methods can be cha
rged
with the task of information flow
based
reasoning. As
a result, the combined use of IF and FCA can shed
some

light on
solving
problems around the IBC
within
the
context

of SDB
,
which is
also harmonious

with

knowledge discovery and representation.

3.3

Prospect of Combined Use of IF and FCA

The e
ssential
element

of
our
IBC
mode for

SDB is
the
combined use of IF and FCA. They
provide

vital

insights

for

our SDB model. The
compatibility

between them
is

crucial for any combined use.
We
give
reasons
below

fo
r using
IF theory and

the t
heory
of FCA

in combination
. Firstly, both
IF

and FCA
share the same origin, i.e., category theory
with the

means of Chu space (Gupta 1994, Barr 1996 and Pratt
1995).
A
s Wolff (2000)

observes
,

it is really
astonishing that these

tools (IF and FCA) are not
mutually

taken into account in
each other’s

theory’
.
Priss (
2005a
) treats the
‘classifications’

in IF as a
general sense of

concept lattice
s’

in FCA.
Following

this line of thinking, secondly,
nearly

all fundamental
concepts in
vented by both of IF and FCA c
an

find
counterparts

in each other.
F
or example,
the
notions of
‘classifications’

in IF
matches that of


formal
context


in FCA;

information channels


in IF
matches


scaled many
-
valued contexts


in Conceptual Scaling
(Ganter

and Wille 1989
,

Ganter and Wille 1999)

associated with FCA. Other basic notions presented in
IF, such as

state space

,

refinement of channels
’, and
ways of handling ‘vagueness’

are also delivered in
FCA
mathematically
(Wolff 2000).
Finally
,
IF

bears
epi
stemological

resemblance to FCA. To be explicit,
starting from the same
algebraic

category, IF
together

with FCA aim at
formulating

and justif
ying


partial
order
’ that
rel
ies on

agreed understanding of
the
exist
ence of ‘
duality


between
separated

situation
s
,

which is exactly why information flow commences.

Combined use of IF and FCA is
beneficial

to
constructing
the
IBC
model

of SDB.
S
DB highly
needs to capture more semantics. In IF

and
FCA
reinforced IBC model, FCA
would

serve as the
linkage between IF
r
easoning

and
the modelled

domain.
Due to the ‘non
-
directly
-
applicable


nature of
IF

(Devlin 1999)
,
applying it

directly to

modeling
informational
semantics proves to be
problematic
. I
n
contrast, a

number of works
stemm
ing

from FCA
around knowledge discover
y and information retrieval
have

been put forward.
F
or
example
, Stumme and his
colleagues

have encouraged
the use of
FCA in
exploration and representation of implied information
and facilitat
ing

the conversion of information into
knowledge (Hereth

et al.

2
000
,
Stumme

et al.

1998).
We
would
use the ‘
Conceptual Scaling


techniques
(Prediger and Stumme 1999, Prediger and Wille 1999)

to combine
FCA
with

IF reasoning

because of FCA’s

logical

equivalence

with

Information Channel

.
The
result
s

of reasoning would

be presented in Concept
Graphs
,

which has
advantages

in representing
semantic
s in partial order.

Also,
a
combined use of IF and FCA
can
satisfactorily
model

more semantic
constraints

identified

by
Hammer

(1987)
.
To tackle information flow, IF insists
on a
nalyzing relations between tokens and types.
According to
the
second principle of information flow,
i.e., ‘information flow crucially involves both types
and their particulars’

(Barwise and Seligman 1997,
P.
27
)
.

Originally and largely following Dretske
(19
81), we thought that semantics are presented on the
type level which further provides the meanings to the
tokens involved in information flow. However, from
the paper of Kalfoglou and Schorlemmer on IF
-
map

(2003a)
, we
find

the important role of tokens, e.g
.,
the
same set of rivers and streams, played in determining
semantics or constraints of the whole system

in terms
of semantic
correspondences

between the types.

We
observe that in fact, Kalfoglou and Schorlemmer has
employed primary thinking
of
FCA in exp
loring
‘intension’ and ‘extension’ of formal concepts within
a given formal context. That is from either set, i.e.,
intensions or extensions; we can define
its
counterpart
in the context
, and thus the formal concepts
.
Therefore, using relations in tokens (
extensions), we
would gain relation of concepts and hence arrive at a
set of constrains, which reflect
a type of
regularities of
the whole system in the given context. This is exactly
how tokens take part in defining
the
semantics of a
system
, and in achie
ving semantic interoperability
.

Further to this point, we envisage that d
uality h
eld

by
both IF and FCA

enables

us to support alternative
ways for
the
user to view even the same
information

in SDB. Start with

the relations
that
reside in types
and we woul
d end up with relation of tokens

and
vice
versa
.
Therefore, depend
ing

on what aim we want to
achieve, we
could

selectively take either tokens or
types
as
our starting point in different analysis.
Explicitly, if we want to solve
the
semantic
interoperabilit
y problem, as Kalfoglou and
Schorlemmer did, we
shall

investigate tokens
-

6

determined relations in order to achieve the relations
on types. On the other hand, if we w
ant

to find out
why and how
data
constructs represents (or conveys)
the information about a
given semantic relation

(i.e., a
relation between some real world objects)
, in most

cases
, we
will
take
the
semantics on types of this
structure as
a
foundation.


4. OUTLINE

OF IF

AND
FCA REINFORCED
IBC MODEL FOR SDB

Based on previous sections, we
can now

start
describing the IF

and
FCA
reinforced

IBC model
designed

for SDB.
We will begin with data schemata
as we believe that original databases and schemata is
too valuable to be retained (
Figure 1).

The original database schema together with a serial of
dep
endencies
held

by
the schema

would be analyzed
by using IF and FCA.
T
his
analysis

needs to be
assisted by obtained initiative business constraints
e.g.
stake holder views,

present
ed

in the
format of scales
,
so that subjectiveness is preserved at this early

stage.
The construction of


information channel


of IF
will

benefit

from
the
technique of

conceptual scaling


of
FCA. The output of
investigation

is a conceptual
space

which contains all the constraints (
semantics
) captured
by every information channel.
This space is called by
us as the

kernel of IBC

. When the user
puts a
query
for
a

piece of information to this kernel, if there is no
direct answer, an inference
will

be
carried out

by
means of a set of ‘information

content inference rules


(Feng and Hu
2002). Then, final results are added into
a
separate

conceptual space following the decision of
the user. Connect
ed

with knowledge
representation

and
management
, the consequent results could be
transformed

using

XML
-
extended Information Flow
Framework (IFF
)
(
http://www.ontologos.org/IFF/The%20IFF%20Langu
age.html
)

language.



Figure 1. Overall Picture of IF

and
FCA Reinforced IBC model

T
here are two most
important

parts of this mode
l
which
show in two boxes in
Figure 1.

To clarify what
actually

happens

inside
of
them, we will use two more
diagrams.


7

I
n
F
igure 2, there
is a detailed process

for arriving at

the
kernel

of IBC. Both primary database
schemata

and instances are translated
into many
-
valued context
by FCA. T
h
en, two
scaling

processes
are performed.
T
he
first

one called

conceptual scaling

.
I
t is based
on
the idea that

embedded

structural

constraints
are used
as scales to construct corresponding IF channels.
T
he
many
-
valued c
ontext
will then

become single
-
valued
context as
a result
.
F
ollowing this, using

dependencies
that are determined by

business
rules
as

the other

scales
, another scaling, i.e.,
the ‘
relational
scaling’
,
will

be
accomplish
ed by a final lattice
layout

also wi
th
a crowd of information channels.
T
he
ultimate

results
are sets of

IF


theories

derived from all of the
channels.
This is what we want to model as the system
regularities.


Figure 2. How to Achieve Kernel of IBC

In addition, another significant par
t in
our model

is
inference on information content (Figure 3)
.
T
he
information content based inference rules are put
forward by Feng and Hu (Feng and Hu 2002).
Furthermore, t
hrough

two MSc
projects

(Wang 2005
and Xu 2005), we
found

that these inference rul
es
can

be justified by theory of IF.
I
n the future,
we will
generalize
these verifications by not only IF but also
FCA.


8



Figure 3. Information Inference Rules (IIR)
5. CONLUSIONS


This paper

represents
our first step towards
satisfactor
ily

modeling a S
DB
by means of
an IF and
FCA reinforced IBC. Three more criteria, i.e.,
extracting information, modeling semantic
constraints and also partially representing
information

flow,
have been

proposed in addition to
traditional SDB requirements. The overall idea

of
the
IBC model for SDB is shown with diagram
s

that
heavily draw on
concepts from both IF and FCA.
This attempt seems worthwhile

for the development
of SDB, and it
is also

compatible with most modern
knowledge management systems, and therefore

relevant t
o the
area of semantic web.

References

Barr, M. (1996).
The Chu construction
. Theory and
Applications of Categories, 2(2):17

35.

Barwise, J. and Seligman, J. (1997)
Information
Flow: the Logic of Distributed Systems
,
Cambridge University Press, Cambridge.

Barwise, J. and Perry
J. (
1983)
.
Situations and
Attitudes
,
Cambridge, Mass.
: Bradford
-
MIT.

Boyed, S. (2003).

A Semantic Database
Management System: SIM.

The University of
Texas at Austin, Department of Computer
Sciences. Technical Report CS
-
TR
-
03
-
43.

Cave
don, L. (1998).
Default Reasoning as Situated
Monotonic Inference
,
Minds and Machines

8.

Checkland, P. (1981).
Systems Thinking, Systems
Practice
. Chichester, UK: Wiley.

Chen, I. A. and McLeod, D. (1989).
Derived Data
Update in Semantic Databases
, Proceedi
ngs of
the fifteenth international conference on Very
large data bases, p.225
-
235, July, Amsterdam,
The Netherlands.

Devlin, K. (1991).

Logic and Information
,
Cambridge.

Devlin, K. (2001).
Introduction to Channel Theory
,
ESSLLI 2001, Helsinki, Finland.

Dr
etske, F. (1981)
. Knowledge and the Flow of
Information
, Basil Blackwell, O
x
ford.

Duží, M
. (2001).
Logical Foundations of Conceptual
Modelling
. In VŠB
-
TU Ostrava.

Duží, M
. (2003).
Do we have to deal with
partiality?.

In Miscellania Logica, vol. Tom V,
45
-
76.

Eisenbarth, T., Koschke, R., & Simon, D. (2001).
Feature
-
driven Program Understanding using
Concept Analysis of Execution Trace
. In
Proceedings o
f the Ninth International Workshop
on Program Comprehension. International
Conference on Software Maintenance.


9

Feng, J. (1998).
The "Information Content’ Problem
of a Conceptual Data Schema
, SYSTEMIST,
Vol.20, No.4, pages 221
-
233, November 1998.
ISSN: 0961
-
8309

Feng, J. (1999).
An Information and Meaning
Oriented Approach to the Construction of a
Conceptual Data Schema, P
hD Thesis,
University of Paisley, UK.

Feng, J. and Hu, W. (2002).
Some considerations for
a semantic analysis of conceptual data schemata,

in Systems Theory and Practice in the Knowledge
Age. (G. Ragsdell, D. West. J. Wilby, eds.),
Kluwer Academic/Plenum Publishers, New York.

Feng, J. (2005).
Conditions for Information Bearing
Capability
, Computing and Information Systems
Technical Reports
No 28, University of Paisley,
ISSN 1461
-
6122.

Fischer, B. (1998
). Specification
-
Based Browsing of
Software Component Libraries
. Proc. Automated
Software Engineering, Hawaii, 246
-
254.

fMRIDC,
The Semantic Database Model
,
http://www.fmridc.org/f/fmridc/dmt/sdm.html
.

Ganter, B. and Wille, R. (1999). Formal Concept
Analysis: mathematical foundations. Springer.
ISBN: 3
-
540
-
62771
-
5.

Godin, R., Gecsei, J., & Pichet, C. (1989).
Design of
Browsing Interfac
e for Information Retrieval
. In
N. J. Belkin, & C. J. van Rijsbergen (Eds.), Proc.
SIGIR ’89, 32
-
39.

Gupta, V. (1994).
Chu Spaces: A Model of
Concurrency
. PhD thesis, Stanford University,
1994.

Hammer, M.,
McLeod
, D. (1981).
Database
Description with SDM: A Semantic Database
Model
,
ACM Trans. Database Syst. 6

(3): 35
1
-
386.

Hereth, J., Stumme, G., Wille, R. and Wille, U.
(2000
). Conceptual Knowledge Discovery in
Data Analysis
. In B. Ganter, & G. Mineau (Eds.),
Conceptual Structures: Logical, Linguistic and
Computational Issues. LNAI 1867. Berlin:
Springer, 421
-
437.

Inf
ormation Flow Framework (IFF) Language,
http://www.ontologos.org/IFF/The%20IFF%20La
nguage.html
.

Jagannathan, D.,

Guck
, R. L.,
Fritchman
, B. L.,
Thompson
,
J. P.,

Tolbert
, D. M. (1988).
SIM A
Database System Based on the Semantic Data
Model
.
SIGMOD Conference
: 46
-
55.

Kalfoglou, Y. and Schorlemmer, M. (2003a).
IFMap: an Ontology Mapping Method based
onIinformation Flow Theory
. Journal on Data
Semantics, 1(1):98

127.

Kalfoglou, Y. and Schorlemmer, M. (2003b)
Usi
ng
Information Flow Theory to Enable Semantic
Interoperability
, In Proceedings of the 6th
Catalan Conference on Artificial Intelligence
(CCIA '03), Palma de Mallorca, Spain, October
2003.

Kalfoglou, Y., Dasmahapatra, S., & Chen
-
Burger, Y.
(2004).
FCA in Kn
owledge Technologies:
Experiences and Opportunities
. In P. Eklund
(Ed.), Concept Lattices: Second International
Conference on Formal Concept Analysis, LNCS
2961. Berlin: Springer, 252
-
260.

Kalfoglou, Y., Schorlemmer, M. (2005).
Using
Formal Concept Analysi
s and Information Flow
for Modeling and Sharing Common Semantics
:
lessons learnt and emergent issues, In
Proceedings of the 13th International Conference
on Conceptual Structures (ICCS2005), Kassel,
Germany, July 2005

Kent, R. E. (2000).
The Information Flow
Foundation for Conceptual Knowledge
Organization
. In: Dynamism and Stability in
Knowledge Organization.

Proceedings of the
Sixth International ISKO Conference. Advances
in Knowledge Organi
zation 7 111

117. Ergon
Verlag, Würzburg.

Kent, R. E. (2001).
The Information Flow
Framework
. Starter document for IEEE P1600.1,
the IEEE Standard Upper Ontology working
Group,
http://suo.ieee.org/IFF/
.

Kent, R. E.

(2002a.)
The IFF Approach to Semantic
Integration
. Presentation at the Boeing Mini
-
Workshop on Semantic Integration, 7 November
2002.

Kent, R. E. (2002b).
Distributed Conceptual
Structures
. In: Proceedings of the 6th
International Workshop on Relational Methods in
Computer Science (RelMiCS 6). Lecture Notes in
Computer Science 2561. Springer, Berlin.

Kollewe, W.
, Skorsky, M., Vogt, F., and Wille, R.
(1994).
TOSCANA


ein Werkzeug zur
begrifflichen Analyse und Erkundung
von Daten.
In R. Wille, &19 M. Zickwolff (Eds.),
Begriffliche Wissensverarbeitung
-

Grundfragen
und Aufgaben.Mannheim: B.I.
-
Wissenschaftsverlag, 2
67
-
288.

Lyngbaek, P. and Vianu, V. (1987).
Mapping a
Semantic Database Model to the Relational
Model
, Proceedings of the 1987 ACM SIGMOD
international conference on Management of data,
p.132
-
142, May 27
-
29, San Francisco, California,
United States.

Malik, G. (2004.)
An Extension of the Theory of
Information Flow to Semiconcept and
Protoconcept Gra
phs
.
ICCS 2004
: 213
-
226.

Miller, R. J., Ioannidis, Y. E. and Ramakrishnan, R.
(1993).
The Use of Information Capacity in
Schema Integration and Translation
, in
Proceedings of the 19th International Conference

10

on Very Large Data Base, Morgan Kaufmann,
San Francisco.

Mingers, J (1995).
Information and Meaning:
Foundations for an Intersubjective Account
.
Journal of Information Systems 5 285
-
306.

Pratt, V. (1995).
T
he Stone gamut: A
coordinatization of mathematics
. Logic in
Computer Science, pages 444

454.

Prediger, S. and Stumme, G. (1999).
Theory
-
driven
Logical Scaling
. Conceptual Information Systems
meet Description Logics. In P. Lambrix, A.
Borgida, M. Lenzerini,

R. Muller, & P. Patel
-
Schneider (Eds.), Proceedings DL’99. CEUR
Workshop Proc. 22.

Prediger, S. and Wille, R. (1999).
The Lattice of
Concept Graphs of a Relationally Scaled Context
.
In W. Tepfenhart, & W. Cyre (Eds.), Conceptual
Structures: Standards and
Practices. Proceedings
of the 7th International Conference.

Priss, U. (2005a).
Formal Concept Analysis in
Information Science
. Annual Review of
Information Science and Technology. Vol 40.

Priss, U. (2005b).
Linguistic Applications of Formal
Concept Analysi
s
. In: Ganter; Stumme; Wille
(eds.), Formal Concept Analysis, Foundations
and Applications. Springer Verlag.
LNAI 3626
,
p. 149
-
160.

Rishe, N.,
Semantic Wrapper over Relational
Database
.
http://n1.cs.fiu.edu/SemanticWrapper.ppt
.

Shimojima, A. (1996).
On the Efficacy of
Representation
, Ph.D. Thesis. The Department of
Philosophy, Indiana University.

Stumme, G., Wille, R., and Wille, U. (1998
).
Conceptual Knowledge Discovery in Databases
using Formal Concept Analysis Methods
. In J. M.
Zytkow, & M. Quafofou (Eds.), Principles of
Data Mining and Knowledge Discovery.
LNAI1510. Berlin: Springer, 450
-
458.

Tsur, S. and Zaniolo, C. (1984)
An implementation
of GEM Supporting a Semantic Data Model on
a Relational Back
-
end
, Proceedings of the 19
84
ACM SIGMOD international conference on
Management of data, June 18
-
21, Boston,
Massachusetts.

Wang, X. and Feng, J. (2005b.)
The Separation of
Data and Information in Database System under
an Organizational Semiotics Framework
. The 8th
International W
orkshop on Organizational
Semiotics, Toulouse, France.

Wang, Y. and Feng, J. (2005a).
Verifying
Information Content Containment of Conceptual
Data Schemata by Using Channel Theory
. The
14th International Conference on Information
Systems Development, Karl
stad, Sweden.
Springer
-
Verlag.

Wille, R. (1982).
Restructuring lattice theory: an
Approach based on Hierarchies of Concepts.
In I.
Rival (Ed.), Ordered sets. Reidel, Dordrecht
-
Boston, 445
-
470.

Wille, R. (1992).
Concept Lattices and Conceptual
Knowledge Sys
tems
. Computers & Mathematics
with Applications, 23, 493
-
515.

Wille, R. (1997a).
Conceptual Graphs and Formal
Concept Analysis
. In D.Lukose, H. Delugach, M.
Keeler, L. Searle, & J. F. Sowa (Eds.),
Conceptual Structures: Fulfilling Peirce’s Dream.
Proc. ICC
S’97. LNAI 1257. Berlin:Springer,
290
-
303.

Wille, R. (1997b).
Introduction to Formal Concept
Analysis
. In G. Negrini (Ed.), Modelli e
modellizzazione. Models and modelling.
Consiglio Nazionale delle Ricerche, Instituto di
Studi sulli Ricerca e Documentazio
ne Scientifica,
Roma, 39
-
51.

Wobcke, W. (2000).
An Information
-
Based Theory
of Conditionals
.
Notre Dame Journal of Formal
Logic 41
(2): 95
-
141.

Wolff, K. E.

(2000).
Information Channels and
Conceptual Scaling
. In Working with Conceptual
Structures. Contributions to ICCS 2000, Shaker
Verlag.

Xu, H. and Feng, J. (2002).
‘The

"How" Aspect of
Information Bearing Capability of a Conceptual
Schema at the Path Level’
.
The 7th Annual
Conference of the UK Academy for Information
Systems, UKAIS'2002. Leeds . ISBN 1
-
898883
-
149, pp.209
-
215

Xu, Z. (2005).
Verifying Information Inferenc
e Rules
by using Channel Theory
, MSc dissertation,
University of Paisley.













Wang.Y is a Researcher and Dr.
Feng
J.

a Senior
Lecturer at the University of Paisley