Semantic Business Analysis Model
Considering Association Rules Mining
Anna Rozeva
1
, Boryana Deliyska
1
and Roumiana Tsankova
2
1
Department of Business Management, University of Forestry, 10 Kliment Ohridski blv., Sofia, Bulgaria
2
Department of Management, Technical University, 8 Kliment Ohridski blv.Sofia, Bulgaria
Keywords: Business Analysis, Knowledge Discovery in Databases, Association Rules, Domain Ontology, Semantic
Model, Ontology Reasoning.
Abstract: Deriving models for intelligent business analysis by generation of knowledge through data mining
techniques has proved to be highly theoretically researched and practically implemented topic in the field of
decision support and business intelligence systems in the last decade. A general data mining task concerns
discovery and description of relationships among items recorded in business transactions. The model of
association rules is the one most implemented for revealing such relationships. In order to increase the
decision support value of the output associative models the necessity for capturing and involving semantics
from the domain of discourse has emerged. Ontologies represent the tool for structuring the concepts and
their relationships as knowledge for a subject area that was established with the growth of the Semantic
Web. The paper is intended to design a framework for implementing ontologies in the association rule
analysis model that provides for involving semantics in the extracted rules by means of initial verification
and optimization of the mining task by database scheme ontology and exploration of rules’ interestingness
by ontology reasoning process.
1 INTRODUCTION
Modern business analysis is inevitably information
based. Therefore it faces the problem of dealing with
continuously growing amounts of structured or
unstructured data from a variety of sources. The
main challenge consists in getting the “big picture”
out of it for the sake of best serving the decision
making. The general technology for processing data
resulting in deriving summarized models is data
mining (Larose, 2006). Models contain knowledge
about a related domain. Mining tasks perform
mainly classification, clustering or extraction of
associations. The model describing associations
between items on the basis of their mutual
occurrence in transactions has proven to be of
particular value for business analysis. It’s
represented by rules relating certain items X and Y
with assigned support and confidence (Maragatham
and Lakshmi, 2012). These rules represent new
knowledge and are derived by processing raw data
from transactions by data mining techniques which
is also often referred to as knowledge discovery in
databases (KDD) (Frawley et al, 1992). Models
extracted by summarizing significant amounts of
data are related to instances of objects from the
domain of discourse and hence they lack the
abstractness that is inherent to models in general.
At the same time knowledge about domains exist
which is accumulated, stored for being used and
shared through the resources of the Semantic Web.
The knowledge represents conceptualization that
articulates abstractions of certain state in reality
(Guizzardi, 2007). The tool for its engineering is
referred to as ontology. Obviously ontologies
capture the domain semantics. The task is to map
ontologies to extracted analytical models for
verifying their correctness and for inferring new
knowledge. “The role to be played by ontologies in
KDD (and even their mere usability) depends on the
given mining task and method, on the stage of the
KDD process, and also on some characteristics of
the domain and dataset” (Svatek and Rauch, 2006, p.
163). We propose a framework for implementation
of domain ontologies in association rule mining with
the goal for mining task verification on the database
scheme, extracted rules conceptualization and
further refinement through domain ontologies.
122
Rozeva A., Deliyska B. and Tsankova R.
Semantic Business Analysis ModelConsidering Association Rules Mining.
DOI: 10.5220/0004461801220127
In Proceedings of the Second International Symposium on Business Modeling and Software Design (BMSD 2012), pages 122-127
ISBN: 978-989-8565-26-6
Copyright
c
 2012 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

The remainder of the paper is organized as
follows: The second section is a review on
approaches for application of ontologies in mining
databases for association rules extraction and results
obtained. The third section presents a framework for
extracting semantic association rules analysis model
for knowledge generation from a database by
ontology reasonong. The fourth section presents
application results on sample database and ontology
instantiations. The last section concludes with
discussion on the effect for the semantic enrichment
of the business analysis model.
2 ASSOCIATION RULES AND
ONTOLOGIES
Association rule as defined by (Agrawal et al., 1993)
is a triple (I, S, C), where I is an implication of the
form X
→ Y denoting if X then also Y, S and C are
interestingness measures for support and confidence.
X and Y are items in database transactions and the
rule correlates the presence of both sets of items in
transactions, i.e. transactions that contain items of X
tend to contain items of Y as well. S indicates the
statistical significance indicating the percent of
transactions that contain items of both X and Y. C
measures rule’s strength as the probability of their
mutual occurrence. The extracted association rules
have support and confidence greater than the
predefined ones.
Ontology is represented in (Maedche, 2002
) as 5-
tuple of the form (C, R, H
C
, F, A), where C is a set
of concepts, R is a set of nonhierarchical relations
among concepts, H
C
is taxonomy of the concept
hierarchy that defines relations among concepts c
1
and c
2
of type ‘is-a’ and 'has-a' mainly. F is a
function that instantiates the relationships from R
and A is a set of axioms that describes constraints.
The definition is a formal description of the concepts
and their hierarchical relationships in a specific
domain as a piece of reality. Further on it’s
instantiated for an element of the domain by
application ontology. Task ontology is designed and
implemented with the purpose of modelling the
knowledge for solving specific task within the
application as shown in (Deliyska and Manoilov,
2012).
The ontology support of the KDD process has
been within the scope of a lot of studies recently. In
(Gottgtroy, Kasabov and MacDonell, 2004) a
general framework for the mutual interdependence
of ontology building and maintenance and the
knowledge discovery is suggested. It’s argued that
ontologies facilitate each stage of the knowledge
discovery process from improving the quality of the
source data, feature selection by navigation through
hierarchy and finally production of improved results
by reasoning within ontology’s links and
relationships.
Framework for implementation of domain
knowledge into association rules generation is
proposed in (Antunes, 2008). It provides for
formulation of constraints that control the mining
process by using domain ontology. Two constraint
types are defined, i.e. interestingness and content.
While the interestingness measure refers to
quantitative conditions on the frequency of mutual
appearance of the items in transactions, the content
constraint considers characteristics of the items that
are present in the domain ontology. They are
qualified as taxonomical when based on restriction
among concepts, defined in the ontology taxonomy.
Non-taxonomical constraints are referred to as
relational and they are based on the ontology
relations among concepts. The constraints guide the
knowledge discovery process, providing the desired
level of abstraction.
The framework presented in (Bellandi et al.,
2007) provides for the extraction of constraint-based
multilevel association rules with ontology support.
The constraints for the mining process are defined
from domain ontology as domain specific. The
ontology is used for filtering the transaction
instances sourcing the mining process. The system
architecture involves interpretation module
translating user constraints and passing them to an
ontology query engine for excluding non-interesting
rules and for presenting the interesting ones at the
relevant abstraction level. It’s stated that this
approach improves association rules support and
provides for decreasing the amount of useless rules
discovered when source data are sparse.
While the frameworks discussed so far address
the input of the mining process, the one proposed by
(Marinica and Guillet, 2010) considers the
association rules post mining phase with the aim to
decrease the number of delivered rules so that they
are useful and understandable for the user. An
approach for pruning and filtering the discovered
rules is designed. Ontologies are used for the
integration of user knowledge at the post processing
stage. Besides this the quality of discovered rules
can be validated at different points in an interactive
process by the domain expert.
The notion of multidimensional association rules
has been introduced in (Wu et. al., 2007). The
Semantic Business Analysis Model - Considering Association Rules Mining
123

definition refers to the star scheme of the data
warehouse as source of the transactions for the
mining process. The approach is focused on the
stored data and aims to overcome the lack in their
structural and semantic exploration. It proposes
functions for the effective maintenance of the
discovered knowledge. The stated problems are
solved by designing two types of ontologies. The
scheme ontology contains the warehouse metadata.
The domain ontology constructs the domain
knowledge for the mining subject as conceptual
layer and relationships among the related concepts.
They are implemented at loading data in the
warehouse. It’s pointed out that by this approach
minimization of data mining searching boundaries
and prevention of repeated mining are achieved. On
the other hand by extending the association rule
mining to items from the domain ontology
generalization of items to concepts with richer
semantics is achieved.
In our previous work (Rozeva et. al., 2011)
we’ve designed a framework for generation of
knowledge models from text documents which
consisted of structure and knowledge models.
Current work extends the categorization knowledge
model presented there by exploring the association
rules model. It is designed on mining set containing
transaction items and acquires business semantics by
reference to ontologies. The results obtained will
support its involvement in mining a text document
corpus. The related work review presented is the
background for designing a semantic analysis model.
3 SEMANTIC ANALYSIS MODEL
DESIGN
The proposed framework implements ontological
reference both in the step of setting up the input to
the association rules mining task and extending the
value of extracted patterns. The functionality
implemented in the task definition step provides for
the optimization analysis of mining task parameters.
It examines the input and predictable item sets and
performs reasoning on designed database scheme
ontology for ensuring non-redundant rule
generation. On the basis of exploring key-based and
hierarchical dependencies included in the scheme
ontology, both the input and predictable item sets
will be optimized. At the evaluation stage obtained
rules are explored by reasoning on provided domain
ontologies. The goal is to put the focus on the
interesting rules. Such rules are considered the ones
with items belonging to different domains. The
proposed architecture is shown in Figure 1.
Figure 1: Framework of semantic analysis model.
The ontological reference at the front and back
ends of the association rule generation process
provides for the reduction of the number of rules
obtained and for enhancing their value for business
analysis purposes.
3.1 Scheme Ontology Design
The scheme ontology contains metadata of the
database scheme. An excerpt of the designed scheme
ontology is shown in Figure 2.
Figure 2: Database scheme ontology.
The top ontology concept SQL Database has
subconcept Scheme and its subconcepts are the
database objects Table and View. Their concept
relationships to the ontology root are ‘Has-a’. The
concepts Table and View have subconcept Attribute.
It is related to the superconcepts by ‘Has-part’
relationship. The Attribute concept which is
subsumed by the Table concept has three
subconcepts, i.e. Primary key, Foreign key and Non-
key, related to it with ‘Is-a’ relationships. The
Attribute subconcept of the View concept has just a
Second International Symposium on Business Modeling and Software Design
124

Non-key subconcept. The primary, foreign and non-
key concepts have properties which are name and
value type. Value types are: numeric, text, date, etc.,
which have 'Is-of-type/Has-type' relationships to the
Attribute concept. The Attribute concept which is
subsumed by the Table concept may represent
hierarchy with levels defined by Attribute concept.
The relationships between the hierarchy levels are of
type ‘Has-part’. For shortness concept instances are
not shown in the scheme ontology.
A mining task MT for association rules specifies
database tables, views or table and view related with
many-to-one relationship; input and predictable
attribute(s). The MT query-like representation
adapted from (Wu et al., 2007) is:
Mine Association Rules
InputSet {IAttr
1
, IAttr
2
,…}
PredictSet {PAttr
1
, PAttr
2
, …}
From CaseTable Inner Join
NestedTable
With MinSup%, MinConf%
The aim of the MT query optimization analysis is
to identify input attributes which are functionally
dependent. The dependency type is specified in the
scheme ontology as being either on the primary key
or between levels in a hierarchy.
3.2 Ontological Optimization of Mining
Query Definition
The optimization of MT definition is proposed to be
performed by reasoning on the database scheme
ontology. A description logic reasoning tool Pellet is
described in (Sirin, E., Parsia, B. et al., 2007).
Reasoning concerns finding implicit facts in the
ontology on the basis of explicitly stated facts. Basic
reasoning tasks refer to proving satisfiability of a
concept, subsumption of concepts, check an
individual as instance of a concept, retrieving all the
individuals that are instances of a concept and
finding all the concepts an individual belongs to.
Answering queries over ontology classes and
instances for finding more general/ specific classes
and retrieving the individuals matching it is a basic
service performed in ontology reasoning. The
reasoning tasks for checking MT definition retrieve
the individuals of the Attribute concept and their
descriptions and axioms. Mining query parameters
are checked to match some of the retrieved
individuals. Further on parameters are checked for
consistency against the descriptions and constraints
of the examined concept. Consistencies on primary
key and hierarchical inclusion are considered. If the
parameter set is consistent on the examined
descriptions then the dependent parameter has to be
removed from the mining query item set. For
shortness parameter set with 2 items is considered.
Further on is the specification of the ontology
reasoning process:
InputParameterSet: {IAttr
1
, IAttr
2
},
SchemeOntology;
Concept→’Attribute’;
Retrieve individuals of ‘Attribute’
→ ABox;
Retrieve descriptions for ABox →
TBox;
Check IAttr
1
∈ABox,
Check IAttr
2
∈ABox;
Check ∃ ’Identifies’.IAttr
1
,IAttr
2
⇒ Outputset {IAttr
1
};
Check ∃ ‘Has-part’.IAttr
1
⊃IAttr
2
⇒Outputset {IAttr
1
}.
The optimization of mining query definition
performed by ontological reasoning process
decreases model’s size and training time by
preventing the generation of redundant association
rules.
3.3 Analysis of Rules Interestingness
The interestingness analysis of mined association
rules has been adapted from (Marinica and Guillet,
2010). We propose to perform it by reasoning on
domain ontologies. The analysis is targeted at:
 Conceptualization / individualization along the
domain ontology taxonomy;
 Filtering obvious rules, i.e. with the same
subsuming concept.
The first task provides for the generalization /
specialization of the left side (condition) and the
right side (consequent) items of the extracted rules
by implementing the subsumption or individual
retrieval reasoning operations on the domain
ontology. This analysis can be applied when the
condition and consequent items refer to the same or
to different domain ontologies. The second task aims
at focusing on non-obvious rules. Obvious are
considered rules with condition and consequent
items having the same subsuming concept. The rule
involving such items represents association between
items from a common domain. The semantic value
that is added by the analysis consists in revealing
associations between items from different domains.
The associations are considered more interesting
when the condition and the consequent domains
differ to the greatest extent possible. The measure
Semantic Business Analysis Model - Considering Association Rules Mining
125

for the differentiation is the number of subsumptions
that are to be performed for reaching their common
subsuming concept as in the following reasoning
process:
InputRuleSet: R, DomainOntology: DO;
R:{Condition, Consequent};
DO:Statement;
[a, b rdf:Statement;
rdf:subject:s;
rdf:predicate:p;
rdf:object:o]
Condition:a.o ∩ Consequent:a.o
⇒ {Condition, Consequent}⊂ a.s;
R → Non interesting;
Condition:a.o;
Consequent:b:o;
⇒ {Condition, Consequent} ⊄ a.s;
R → Interesting.
4 SEMANTIC MODEL
IMPLEMENTATION
The proposed approach for semantic association rule
model design has been implemented on purchase
transaction tables from the sample database
(Microsoft SQL Server Database Product Samples,
2012). The database scheme ontology from Figure 2
has been filled in with the corresponding instances.
Administrative location and product domain
ontologies have been designed. The MT queries that
have been defined and the result from ontological
reasoning analysis are presented in Table 1.
Association rules model has been trained with
the Apriori algorithm with input and predictable
parameters CustId and ProdId. The minimum
probability was set to 10% and minimum support to
1%. The number of generated rules without and with
optimization as shown in the last column is
approximately 50%. By varying the support and
probability values the rule number will be different.
Enhancement of model interestingness is
performed by filtering the non-obvious rules. They
are identified as belonging to different subsuming
concepts. The case of rules that involve single
condition item is considered.
Table 1: Mining task optimization.
MT Input MT Predict Optimization Rules
CustId,
CustName
ProdId CustId,
ProdId
158→49
CustId,
Country
ProdId CustId,
ProdId
111→51
The task is performed by extraction of mined
rules from the model as RDF triples {id, condition,
consequent} from the tree-like structure shown in
Figure 3.
Figure 3: Model’s node content.
The tree nodes are from the following types: with
condition item only, with consequent item only and
with both condition and consequent item values.
Each node has the respective probability and support
values attached. The nodes with both condition and
consequent items are filtered and the RDF triples are
obtained. The extracted subsuming concepts of the
rule items are compared and the rule is either kept or
discarded. An initial model with ProdId as input and
predictable parameter and resulting predicted
associations is shown in Figure 4.
Figure 4: Initial model rules.
By applying interestingness reasoning on the
Product domain ontology where the Product concept
is subsumed by the Category concept the model
shown in Figure 5 has been obtained. The rules
which remained after performing the analysis
display associations between ontologically remote
items only.
Figure 5: Interesting rules.
By subsumption operation on the RDF triples the
rules can be further ontologically generalized.
Second International Symposium on Business Modeling and Software Design
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5 CONCLUSIONS
The analysis model for extraction of associations
between items from business transactions stored in a
database presented in the paper introduces an
innovative approach for capturing and using domain
knowledge. It is intended to filling the gap between
definition of the analysis task and the interpretation
of obtained results and the examined business
domain. Ontologies have been recognized and
widely adopted as model for capturing this
background knowledge. The proposed framework
for designing semantic analysis association rules
model implements two types of ontologies that
provide background knowledge for the data source
structure and the domain of discourse. The ontology
content is made use of by means of reasoning
process based on description logic. The reasoning on
the data source ontology provides for the support
and optimization of mining task definition. Key
dependent and hierarchy related query parameters
are identified by the reasoning process and discarded
for the sake of generating non-redundant set of rules.
Domain ontology reasoning is implemented for
tuning rule interestingness. Interesting rules are
considered those involving items from different
domains. Reasoning process procedures have been
presented. The proposed methodology has been
evaluated on sample transaction database with
reasoning on instantiated structure and domain
ontologies.
Future work is intended in refining the reasoning
process in order to be applied further on to mining
associations between terms extracted from text
document corpus with available ontology referring
to e-Governance services. Application of the
designed framework in automatic generation of
ontologies will be researched as well.
ACKNOWLEDGEMENTS
The paper presents results of the project “Research
and Education Centre for e-Governance” funded by
the Ministry of Education in Bulgaria.
REFERENCES
Agrawal, R., Imielinsky, T. and Swami, A., 1993, Mining
Association Rules between Sets of Items in Large
Databases., In Proc. ACM SIGMOD Conf. on
Management of Data, p.207-216.
Antunes, C., 2008, An Ontology-Based Framework For
Mining Patterns In The Presence Of Background
Knowledge, In Proceedings of International
conference on advanced intelligence (ICAI 2008), Post
and Telecom Press, Baijing, China, p.163-168.
Bellandi, A., Furletti, B., Grossi, V. and Romei, A., 2007,
Ontology-driven association rule extraction: A case
study, In Proceedings Workshop Contexts and
Ontologies: Representation and reasoning in 16
th
European conference on artificial intelligence, p.1-10.
Deliyska, B., Manoilov, P., 2012, Ontologies in intelligent
learning systems, In Jin, Q. (ed.), Intelligent learning
systems and advancements in computer-aided
interaction, Information science reference. An imprint
of IGI Global, p.31-48.
Gottgtroy, P., Kasabov, N. and MacDonell, S., 2004, An
ontology driven approach for knowledge discovery in
biomedicine, In Proc. 8th Pacific Rim Int’l Conf. on
Artificial Intelligence.
Guizardi, G., 2007. On ontology, ontologies,
conceptualizations, modelling languages and (meta)
models. In Vasilecas, O. (eds.), In Frontiers in
artificial intelligence and applications, databases and
information systems IV, Amsterdam, The Nederlands:
IOS Press, p.18-39.
Frawley, W., Piatetsky-Shapiro, G., and Matheus, C.,
1992, Knowledge Discovery in Databases: An
Overview. AI Magazine, Vol. 13, p.57-70.
Larose, D., 2006. Data mining methods and models,
Wiley-IEEE Press.
Maedche, A., 2002, Ontology Learning for the Semantic
Web, Kluwer Academic Publishers.
Maragatham, G., Lakshmi, M., 2012, A recent review on
association rule mining, Indian journal of computer
science and engineering, Vol. 2(6), p.831-836
Marinica, C. and Guillet, F., 2010, Knowledge-Based
Interactive Postmining of Association Rules Using
Ontologies, IEEE Transactions On Knowledge And
Data Engineering, Vol. 22, No. 6, p. 784-797.
Microsoft SQL Server Database Product Samples,
AdventureWorks2008_SR4, http://msftdbprodsamples.
codeplex.com/releases/view/37109, Retrieved March
24
th
2012.
Rozeva, A., Ivanov, M. and Tsankova, R., 2011, Business
modelling for generation of knowledge from explicit
data, In Proceedings of First International Symposium
on Business Modeling and Software Design
(B.Shishkov, ed.), Sofia, Bulgaria, p.114-121
Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A. and Katz,
Y., 2007, Pellet: A Practical OWL-DL Reasoner, Web
Semantics, 5 (2), p.51-53
Svatek V., Rauch J., 2005, Ontology-Enhanced
Association Mining. In: Semantics, Web and Mining,
Joint International Workshops, EWMF 2005and KDO
2005, p.163–179.
Wu, C.-A., Lin, W.-Y., Tseng, M.-C. and Wu, C.-C.,
2007, Ontology-Incorporated Mining of Association
Rules in Data Warehouse, Journal of Internet
Technology, Vol.8, No4, p.1-9.
Semantic Business Analysis Model - Considering Association Rules Mining
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