AN AGENT-BASED INFORMATION CUSTOMIZATION
SYSTEM USING CBR AND ONTOLOGY
Hyun Jung Lee
Korea University Business School, Anam-dong Seongbuk-gu, Seoul, 136-701, Korea
Mye M. Sohn
*
Department of Systems Management Engineering, Sungkyunkwan University
300, Chunchun-dong, Jangan-gu, Suwon, Kyunggi-do, 440-746, Korea
Keywords: Case-based Reasoning, Ontology, Agent-based System, Information Customization.
Abstract: An Agent-based Information Customizing System is composed of a Case Generation Agent to transform
unstructured documents to a structured form such as cases, and a Case Customization Agent to select the
most similar case from the case-base and adjust the selected case depending on the user’s information
requirements. The developed case contains features and their values, and each defined case that is based on
the information can each has a different feature set. Thus, it is possible to represent the contained details in
documents, and it is easier to find more appropriate information from the case pool. Two-step similarity
calculation using features and values and domain ontology are applied to find an appropriate case. A case
customization process is suggested to adjust the case. In our future work, the suggested system would be
applied to traveller’s systems to integrate information and will prove its effectiveness.
1 INTRODUCTION
The information users are focusing on the speedy
collection, the appropriate processing and the utility
of the information on the web. Information users
have recently been able to obtain the needed
information from a variety of resources. However, it
is not easy to find customised information from
these resources. Until now, information resources
such as the Web, don’t have the ability to
completely filter out the customised information that
may be obtained from inaccurate or excessive
information pools according to the information users’
requirements. For example, most travellers spend a
lot of time and effort to find customised travelling
information that suits each traveller’s purpose via
the Web or the Internet (Pan B., and D. R.
Fesenmaier, 2006). It’s true that it is too difficult to
select, customize and integrate the information based
on unstructured documents without the help of the
information worker.
In this paper, we propose an agent-based
information customization system that supports
select and integrating the dispersed information on
the Web depending on the information user’s
requirements, which is composed of the
transformation phase and the customization phase.
The transformation phase transforms the
information or the data, which is collected from a
variety of information resources, into a structured
data format for an intelligent agent’s processing. As
a structured format, cases may be appropriate to
transform unstructured documents into structured
documents. Even though we consider other
structured formats such as rules using if-then format,
frames or logic using taxonomical expression of
knowledge, etc., when we transform unstructured
data or a document into a rule or a frame format, we
will most likely lose a lot of information.
There are two reasons why we chose the case
as a structured data format. First, information users
usually want to refer to qualified information that is
based on other users’ experiences and accumulated
knowledge. Information users have recently
preferred to use web-based information for reference.
They want to perform web searches to build
customised cases through retrieving, reusing, and
*
Corresponding author.
285
Lee H. and M. Sohn M. (2010).
AN AGENT-BASED INFORMATION CUSTOMIZATION SYSTEM USING CBR AND ONTOLOGY.
In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Artificial Intelligence, pages 285-290
DOI: 10.5220/0002723702850290
Copyright
c
SciTePress
revising the accumulated cases on a case-by-case
basis. Second, when we use cases, we have less
information loss than when we use rules or frames.
Cases can include context information that contains
the tacit knowledge within documents, and it will be
helpful to maximize the information users’
satisfaction.
In the customization phase, we generate new
information that satisfies the users’ requirements by
using case-based reasoning. The proposed case-
based reasoning uses the two-step similarity
calculation method. In step 1, the similarity between
the information that the users’ requires and the
features of the cases is calculated by using the
requirements-feature similarity method. In step 2,
the requirement-value similarity is used to calculate
the similarity between the details of values and the
users’ requirements.
The proposed case is structured by features and
values. A value is defined by sentences that include
features. Therefore, a feature can be matched into
several sentences as values. To find a similar case,
we filter the cases according to the similarity
between case and the information of the user’s
requirements. Ontology is needed to determine the
semantic relationship between the features or values.
The selected case through the two-step similarity
calculation can be adjusted according to the user’s
requirements, which is conducted by the Case
Customization Agent.
In chapter 2, after review related studies, in
chapter 3, the overall architecture of the proposed
system is introduced. Next, we discuss the case and
the sub-case generation, and we describe how to
retrieve similar cases with using the two-step
similarity calculation. In chapter 6, we introduce the
case customization agent. Finally, we discuss future
study and the conclusion of this study.
2 LITERATURE REVIEW
Information integration is the process of
semantically and syntactically combining the data
that comes from different resources with adhering to
a unified format (Bernstein A. Philip and Laura M.
Haas, 2008). To do this, it is important to classify
the information resources such as the Web
documents into structured or unstructured forms.
The former are needed for transforming the data,
which is obtained from databases with the
heterogeneous schemas, into homogeneous data with
using a semantically and syntactically. This can be
accomplished by the ETL technology or the
mediated schema (Halevy Y. Alon, et al., 2005;
Bernstein A. Philip and Laura M. Haas, 2008). The
latter is able to extract information from
unstructured document through keyword search,
summary data, and pre-defined information
structures (e.g., the customer’s name and address,
the product information and the brand name, and so
on). An annotation such as the Resource Description
Framework (RDF) can also be applied to the
information selection and integration processes. The
process of applying ontology to the semantic
integration of information is getting more popular
regardless of the structure of that information
(Alexiev Vladimir, et al., 2005).
However, It is too difficult to integrate information
by using the selected documents based on a keyword
search due to the increase of web documents from
around the world by geographic progression. Even
we use the RSS, we can still receive the selected
information; so, the information users have to have a
part in electing the appropriate information. In
addition, it is necessary to check and resolve
possible conflictions. Information users usually
spend a lot of time and effort to get the customized
information (Mani, I. and Bloedorn, E., 1998; Teufel,
S. and Moens, K., 1997.). Therefore, it is critical to
propose an adequate methodology of information
integration. However, information is basically
differentiated from physical products (Lee, Y., et al.,
2001; Karmarkar, U. S. and Apte, U. M., 2007).
Therefore, to integrate information, research is
needed to understand the contents in documents, to
classify those contents and to integrate the contents.
To accomplish this, we suggest an Agent-based
Information Customization System with using the
CBR and Ontology.
3 OVERAL ARCHITECTURE OF
AGENT-BASED INFORMATION
CUSTOMIZATION SYSTEM
In an Agent-based Information Customization, Case
Generation Agent is for transforming the collected
information into cases, and Case Customization
Agent is for customizing the information depending
on the information of the user’s requirements. Their
overall architecture is shown in Figure 1.
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286
Figure 1: Architecture of agent-based information
customization system.
z The Case Generation Agent is invoked whenever
the RSS reader has requested to receive new
documents. we assumed that the information is
usually collected through a push-based RSS
reader. The agent transforms the unstructured
documents into structured ones, such as cases.
The received unstructured web documents can be
transformed to a case that is based on tags in the
documents. The features of the case are replaced
by tags in the documents, and the value of the
feature is defined by sentences that include the
feature. Each feature can be included in each
sentence, and the sentence is defined by a sub-
case.
z The Case Customization Agent is invoked to
receive the information the user’s requires and to
select similar cases from the case base. A
similarity calculation module calculates the
similarities between a user’s requirements and a
feature set of a case, and between a user’s
requirements and the values. For the similarity
calculation, we usually refer to the domain
ontology. A selected similar case is customised
according to a user’s requirements with
considering the constraints among the
information.
4 CASE GENERATION
The transformed cases from the gathered
unstructured web documents have information
structure. In the initial stage, a feature set of a case is
defined by tags that are annotated on the document,
and then the document is defined as a value of the
feature set. If a document has the same feature set,
then it can be added as another element to the value
set. The transformed case could be broken up into
several sub-cases. The sub-case can have a feature
set and the values. The created cases and sub-cases
can be applied as units to generate new information.
Figure 2 illustrates this process:
Figure 2: Feature and feature-term generation.
Figure (2-a) shows a mapping between tags (i.e.,{t
1
,
t
2
, t
3
, …, t
n
}), which are attached on the original web
document. The tags are defined as a feature set {f
1
f
2
f
3
…f
n
} in a case. The tag set doesn’t contain
any relationships between the tags, but the feature
set represents the features and the relationships
among the tags. Figure (2-b) illustrates the newly
created feature sets. Each feature set has every
relationship between features in a sub-case, such as
({f
1
f
2
} {f
1
f
3
} {f
1
f
2
f
3
} … f
i
f
j
…f
n
}). Figure (2-c) shows the meaningful terms
that are included in each sentence as values. The
meaningful terms are defined by the classes and the
instances of Ontology.
To transform the collected web document into a
case, the following symbol is defined:
f
i
: a feature where it composes of a feature set of a case
v
i
: a value where it composes of a value set of a case
t
i
: a tag where it composes of a tag set of a document
s
i
: a sentence where it composes of a document
k
i
: a term keyword where it composes of a term set of a
value
feature_set {f
i
| i ≥ 1}
value_set { v
i
| i ≥ 1}, v
i
{ s
i
| i ≥ 1}
term_set { k
i
| i ≥ 1}
negarion_sign {NOT|null}
In this research, the suggested case is composed
of a ‘Feature_set’, a ‘Value_set’ and a
‘Negation_sign’, such as Case = {Feature_set,
Value_set, Negation_sign}. The ‘Feature_Set’ is
assumed that there are ‘and’ relationships between
tags, such as Feature_set = {t
1
t
2
t
3
…., t
n
}. The
‘Negation_sign’ shows if the value contains a
positive meaning or not. The case is illustrated as
follows:
AN AGENT-BASED INFORMATION CUSTOMIZATION SYSTEM USING CBR AND ONTOLOGY
287
Case = {feature_set, value_set, negation_sign};
= {{f
1
f
2
f
j
… f
m
}, {v
1
, v
2
,…, v
n
}, {null}};
A case could be broken up into sub-cases that were
the sentences of an original document. Each
sentence can include each representative feature set
that is selected according to ontology (thesaurus).
The sub-case is illustrated as follows:
Sub-case = {feature_set, value_set, negation_sign}
= {{f
1
f
2
f
j
…f
k
}, {s
1
, s
2
,…, s
l
}, {null}}
In sub-case, a value set could be composed of
several sentences and a value of a case could be
decomposed by several sentences. Each sentence is
defined by the Sub-case. Table 1 illustrates the
algorithm for the generation of a case and a sub-case.
Table 1: Algorithm for Cases and Sub-case generation.
5 CASE RETRIEVING USING
TWO-STEP SIMILARITY
In this study, we assumed that the information user’s
requirements are sent to the agent-based information
customization system. The similarity calculation
module of the case customisation agent is invoked
whenever the agent receives new documents.
5.1 Requirement-feature Similarity
The requirement-feature similarity (RF-similarity) is
applied for selecting the most similar case,
depending on the features, between the user’s
information requirements (with vector form) and the
feature set. The details of the requirement-feature
similarity are as follows.
First, the feature sets of cases are categorised
into two groups by comparing information user’s
requirements with the feature sets of the cases. The
one consists of the features that have exactly
matched elements according to the user’s
requirements; the other consists of mismatch
elements with user’s requirements. If the feature set
semantically subsumes an element of the
requirements, then the features are grouped into the
first group. For instance, if an element of the
requirements is ‘Seoul’ and a feature set of a case is
(Seoul, Korea), then the element is identical with the
feature. The conceptual similarity for an exactly
matched group is denoted in the first term of
equation (1). The second term of equation (1) refers
to the conceptual similarity between the features and
the elements that have different values. We obtain
the elements of the conceptual similarity between a
user’s requirements vector and the cases by using the
weighted sum of the similarities between elements
of the user’s requirements and the features of the
cases.
,
,
,
,
,,…,
,
,
,,…,
(1)
The cs(r, c
i
) represents the similarity between a
user’s requirements and the i
th
case.
and
represents the weights of the exactly matched terms
and the mismatched terms. The cs(r
k
, f
ij
) represents
the conceptual similarity between the k
th
element in
the requirements and the j
th
feature of the i
th
case.
The conceptual similarity calculation methods are
feature-based, information contents-based and
hybrids (Slimani T., B. Ben Yaghlane, and K.
Mellouli, 2006, Song Ling et al., 2007). In this study,
the edge-based conceptual similarity method is
applied to the proposed system because it is
intuitional and simple to use. After calculating the
conceptual similarity, if the similarity is over the
threshold value δ, then the value similarity between
the user’s requirements and the selected cases is
calculated
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5.2 Requirement-value Similarity
The calculation of the requirement-value similarity
(RV-similarity) uses the cases that are selected from
the requirement-feature similarity calculation to find
the most similar value. If the selected case from the
requirement-feature similarity doesn’t satisfy the
user’s requirements, then we have to find the most
appropriate value from the sub-cases. To find the
most appropriate value from the selected values, the
domain ontology is applied. First of all, we parse
the selected values into term sets which are
composed of classes or instances in domain ontology.
To determine value of requirement, we calculate
semantic distance between requirements and term
sets. If the value v
ij
of
sub-case i is composed of k
number of terms t
ijk
, then the semantic distance
between a feature and a value v
ij
is calculated by
formula (2). The number of edges between a feature
node and the term nodes in the domain ontology is
expressed by |n
ijk
- n
feature
|.
,
∑
|
| (2)
6 CASE CUSTOMIZATION
In the customization process, we adjust the selected
case to get better customized information. Table 2
shows the algorithm.
The details of each step in this algorithm are as
follows:
Step1: Case Adjustment
z If the selected case includes extra information
that is not included in the user’s requirements,
then delete this from the selected case. To do
this, find the feature of extra information and
delete the sentences that include the features.
z If the selected case doesn’t satisfy the user’s
requirements, then define the required features.
Select the sub-cases that contain the features.
The RV-similarity is processed to find the most
similar value from values of the sub-cases.
Step2: Confliction Check
After adding or deleting a feature and a value in the
earlier case, depending on the user’s information
requirements, it is necessary to check whether or not
any conflict takes place among the contexts. As we
have already mentioned, the relationship between
the features in a feature set of a case is assumed to
be an ‘and’ relationship such as {f
1
f
2
f
3,
…. f
n
}.
Table 2: Algorithm for a Case customization.
However, according to deletion or updating, it is
possible to build new relationships between features.
A confliction check is as follows. Feature f
1
is
an element of a feature set = {f
1
f
2
f
3
f
4
, …, f
n
},
and value v
1
is matched by feature f
1
and has a term
set={k
1
,
k
2
, k
3
,k
4
, …, k
p
}. Feature f
2
is an element of
a feature set = {f
1
f
2
f
3
f
4
,…, f
n
}, and value v
2
is
matched by feature f
2
and it has a term set={k
1
,
k
2
, k
3
,
k
4
, …, k
p
}. The term set of value v
1
is compared
with the term set of value v
2
. The negation sign is
used to check for conflict that happens among values
even though these values contain the same keyword
term set. To check the negation among values, the
keywords of the term sets are compared and the
similarity between them is measured. If the
similarity is over the threshold δ, then the confliction
is defined between them. The confliction between
the features is illustrated as {f
1
f
2
f
3
f
4
, …, f
n
},
{(f
1
f
2
)
f
3
, …, f
n
}, and so on.
AN AGENT-BASED INFORMATION CUSTOMIZATION SYSTEM USING CBR AND ONTOLOGY
289
Step3: Conflict Resolution
If a confliction happens between feature f
i
and
f
j
, then
it is necessary to determine which feature can be
deleted from the feature set. Three possible
resolutions are as follows.
z Delete the conflicting features and the values
from the feature set and value set of the
adjusted case.
z If a confliction happens between f
i
and f
j
, then
compare the term set of the value of the
conflicted features with the user’s requirements.
Delete the less similar feature and value from
the feature set and value set according to the
requirement-value similarity check.
z If a feature has a confliction with one more
value of features such as {(f
i
f
j
)
f
k
}, then it
means that there are conflictions between f
i
and
f
k
such as (f
i
f
k
) and f
j
and f
k
such as (f
j
f
k
).
Delete the feature f
k
from the feature set. So, if
a feature is conflicting with one more feature,
then delete the feature from the feature set.
Step 4: Update the Newly Generated-case to the
Case-base
Update the case-base with the newly adjusted case,
which include the new feature set and the value set.
7 CONCLUSIONS AND FURTHER
RESEARCH
The agent-based information customization system
contains two types of agents for information custom-
tailoring. The case generation agent supports the
transformation of documents as information
resources into cases as structured information. The
case customization agent uses the two-step similarity
calculation and the case customization process.
In this research, the cases and sub-cases are
composed of features and values. The similarity
check to select the most similar case is based on RF-
similarity and RV-similarity. In addition, ontology is
applied to the similarity check to differentiate the
similarities between the differently expressed
features. To adjust the selected case, the
customization process is suggested
The proposed system will be useful to
information users who usually need to customise
various types of information from a variety of
resources. For instance, this system can be applied to
travellers who usually customize travelling
information systems to obtain qualified information.
For future work, we are planning to apply the agent-
based information customization system to a
travelling information system to prove the
effectiveness of our proposed system.
ACKNOWLEDGEMENTS
This work was supported by Defense Acquisition Program
Administration and Agency for Defense Development
under the contract UD080042AD, Korea.
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