This document discusses integrating enterprise and data mining ontologies to extract actionable knowledge. It notes that existing data mining techniques provide large volumes of knowledge but much of it is not useful for making business decisions. The objectives are to 1) design an artifact to formally apply business understanding in data mining and 2) semi-automate the business understanding phase to help users. The expected outcomes are an enterprise ontology and relations between enterprise and data mining ontologies to bridge the gap between business needs and data mining results.

1. Enterprise and Data Mining Ontology Integration to Extract
Actionable Knowledge
HamidReza Nazari1
, MohammadTaghi TaghaviFard1
and Iman Raeesi Vanani1
1
Department of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran
Hamidnazary2002@yahoo.com, {Taghavifard, imanraeesi }@atu.ac.ir
1 RESEARCH PROBLEM
Today, it is hard to imagine an enterprise with no
manipulated processes to manage its data and
information. Organizations invest their money and
efforts in Information Technologies (IT) with the
aim of gaining competitive advantages.
Knowledge Discovery in Databases (KDD) is
one of the most attractive notions to make real-time
and appropriate decisions and to gain sustainable
competetive advantages. Knowledge discovery in
big data era is expected to change the global
economy by 2020, raising 325 billion dollars only in
U.S retailing and manufacturing sectors(McKinsey
& Company, 2013).
Despite this, knowledge discovered by data
mining (DM) algorithms are not actionable enough
to grow the economy (Cao, 2012; Way & Wang,
2008). Feritas(2006) in an investigation of
association rules reported that only 1% of the
outcomes is actionable, 220 rules among 29050
extracted rules. Existing mining techiques and
algorithms have reached considerable levels of
efficiency but their effectiveness on identifying
actionable knowledge is impaired (Antunes, 2014).
DM Researchers have focued more on providing
creative algorithms and investigated the outcomes
from accuracy and efficiency point of view. They
have simplified the problem, not identifying
business requirements and context completely (Cao,
2012; Hirsh, 2008). The knowledge discovered by
algorithms is mainly passive and is not quite active
and helpful for managers to make a decision. So,
more proccesing and analysis is needed to create
actionable knowledge.
This gap is rooted in neglecting business
understanding (BU) in formulating a DM problem.
There is a need to shift from data driven DM to the
domain driven one (Cao, 2010, 2012). Mariscal et al
(2010) in a deep review of DM methodologies and
models, mention these phases to develop CRISP-
DM, to address its weaknesses in BU and deploying
results: 1) Domain knowledge elicitation to
constrain the learning algorithms search space and to
reduce the number of patterns discovered, 2)
Problem specification to better understand the
problem and at the same time, it has to be considered
that objectives and goals are dynamic due to changes
in the requirements of the organization or business,
and they must be reviewed and updated regularly,
3) Interpretation to ensure achieving business
requirements, 4) Automation for supporting non-
expert data mining users to get new knowledge from
new data and existing models in an easy way, 5)
and, Maintenance.
Thus, the research problems are 1) How to
formalize BU in DM process? 2) How the
formalization would lead to actionable knowledge?
2 OUTLINE OF OBJECTIVES
The objectives of this research involve the
following:
1) Designing an artefact to formally apply BU
in DM.
2) Semi-automating BU phase of DM to help a
wide range of users to avoid common
analytical mistakes.
3) To facilitate assessing and deploying
knowledge discovery algorithms results in
business context.
3 STATE OF THE ART
Output of BU determines relevant data for data
understanding phase. It determines if target variable
should be discretized in data preparation phase. It
results in enumeration of applicable modelling
techniques in data modelling phase. And finally, it
determines the evaluation criteria and actionability
of the results (Sharma & Osei-Bryson, 2015).

2. Cespivova et el (2004) outlined the possible roles
of a domain ontology in all phases of data mining
process. Enterprise ontology models an enterprise in
the form of information system. The activities of BU
phase span across concepts and their relationships
are described in an enterprise ontology. Sharma &
Osei-Bryson (2008) assert that no other framework
has the ability to account for BU phase activities. Li
(2014) assert that to support decision in BU, case
based reasoning, ontology based approach and work
flow management reasoning can be useful. Antunes
and Silva (2014) also emphasize using domain
ontology to improve DM effectiveness (Fig 1).
Insert Figure 1 here
Many DM ontologies have developed in the last
decade, like DMOP, ONTO-DM, DMW3, to name a
few. DM ontologies have the ability to select best fit
algorithms among the pool of DM algorithms. Most
of them have neglected BU and focused on data
understanding, preparation and modelling. Table 1
summarises coverage of DM ontologies based on
CRISP-DM.
Insert table 1 here
In summary, 1) This research attempts to match
enterprise and DM ontologies to achieve the
aforementioned objectives. According to our
literature review, no similar research can be found in
the literature. 2) This research will cultivate the
theoretical foundations of enterprise engineering,
DM and ontology engineering by designing an
integrated artefact to formalize BU of DM process
and to facilitate extracting actionable knowledge. 3)
Theoretical contribution of the research will enhance
the interoperability of DM process. 4) The research
is a step forward to automating DM process more
wisely and to helps business users and DM experts
to convert their ad hoc reasoning to more systematic
and ontology aware one.
4 METHODOLOGY
Simon (1969) made a clear distinction between
natural sciences and sciences of artificial or sciences
of design. Artefacts are purposely designed artificial
things to satisfy human’s goals. An artefact is an
interface between the substance and organization of
itself and also external environment in which it
operates.
Information system discipline is rooted in the
creation of information technology artefacts (Hevner
et al, 2004). Thus, this research will apply design
science approach. To build an artefact, we will
review current enterprise and DM ontologies. It is
expected to find basic concepts and relationships
essential for integration in this step. The construct
will be evaluated by experts based on its
completeness, understandability and explicitness.
Thus, the integrated ontology will be built and
evaluated in this step.
Solving the identified problem is one the main
disciplines of design science research methodology.
Therefore, we will use an ontology integration
methodology to build a prototype in OWL or XML.
Alignment and correspondence of the prototype
have to be evaluated in this step.
Finally, an instance for an enterprise unit will be
built based on the prototype. Case study is the
method of evaluation here. We will not directly
compute actionability, instead alignment of the
extracted knowledge and the business goal will be
indicated the actionability of the outcomes.
5 EXPECTED OUTCOME
Achieving the research objectives requires a
developed enterprise ontology that can be
seamlessly integrated with DM ontology. So, the
first outcome of the research is developed enterprise
ontology. In term of design science research
methodology, this is the built construct of the
research. This ontology will formalize concepts
mentioned in BU phase of CRISP-DM like business
objectives, requirements and constraints. Also, it
will be useful in automating data understanding
phase of CRISP-DM.
Defining interactions between concepts of
enterprise and DM ontologies and developing
relations between them are the second expected
outcome of the research. This construct will be used
by ontology integration methodologies to build the
instance aspect of research artefact.
These interactions and relations will bridge the
semantic gap between business users and DM
experts. This way, the complexity of DM for novice
data miners will be reduced. This complexity is a
main barrier for many organizations applying or
utilizing DM processes and methodology.
According to the developed correspondences and
the built instance, it is expected to prune the
outcomes of DM algorithms more easily and to

3. extract more actionable knowledge based on
semantic similarity computing of them. As a result,
the research outcome facilitates the activities of
evaluation phase of CRISP-DM.
6 STAGES OF THE RESEARCH
The research is in its early stage. We have started to
investigate enterprise and DM ontologies to select or
propose suitable ones for the integration.
REFERENCES
Antunes, C., & Silva, A. 2014. New Trends in Knowledge
Driven Data Mining. Proceedings from ICEIS ’14.
International Conference on Enterprise Information
Systems, 346-351.
Cao, L. (2012). Actionable knowledge discovery and
delivery. Wiley Interdisciplinary Reviews: Data
Mining and Knowledge Discovery, 2(2), 149-163.
Cao, L. (2010). Domain-driven data mining: Challenges
and prospects. Knowledge and Data Engineering,
IEEE Transactions on, 22(6), 755-769
Cešpivová, H., Rauch, J., Svatek, V., Kejkula, M., &
Tomeckova, M. (2004). Roles of medical ontology in
association mining Crisp-DM cycle. In ECML/
PKDD04 Workshop on Knowledge Discovery and
Ontologies (KDO’04), Pisa (Vol. 220).
Freitas AA (2006) Are we really discovering “interesting”
knowledge from data? Expert Update, Vol. 9, No. 1,
41-47, autumn 2006.
Hevner, A., March, S., Park, J., and Ram, S. (2004)
Design Science in Information Systems Research. MIS
Quarterly, 28(1).
Hirsh, H. (2008). Data mining research: Current status and
future opportunities. Statistical Analysis and Data
Mining: The ASA Data Science Journal, 1(2), 104-107
Li, Y. (2014) New Artefacts for the Knowledge Discovery
via Data Analytics (KDDA) Process. PhD
Dissertation, Virginia Commonwealth University,
USA.
Mariscal, G., Marbán, Ó. & Fernández, C. (2010). A
survey of data mining and knowledge discovery
process models and methodologies. The Knowledge
Engineering Review, 25(02), 137-166
Mckinsey and Company. (2013). Game changers: Five
opportunities for US growth and renewal. McKinsey
Global Institute.
Sharma, S., and Osei-Bryson, K. (2015) A Method for
Formulating the Business Objectives of Data Mining
Projects. In Knowledge Discovery Process and
Methods to Enhance Organizational Performance.
Osei-Bryson, K, and Barclay, C. (editors), CRC Press
Simon, H. A. 1969. The Sciences of the Artificial, (The
MIT Press: Cambridge, MA.
Wang, H., & Wang, S. (2008). A knowledge management
approach to data mining process for business
intelligence. Industrial Management & Data Systems,
108(5), 622-634.

4. Figure 1. Research artefact
Table 1. DM ontology comparison
BU Data Understanding Data preparation Modelling Evaluation
Deployme
nt
DMW3
DMOP
ONTO-DM
KDDONTO
Expose
KD-Ontology
Intelligent
Discovery
Electronic
Assistant
(IDEA)
DAMON
Ontological
Learning
Assistant
(OLA)