Decision Tree Transformation for Knowledge Warehousing

Rim Ayadi

1,2

, Yasser Hachaichi

1,2

, Saleh Alshomrani

and Jamel Feki

1,3

Multimedia, InfoRmation Systems and Advanced, Computing Laboratory, Sakiet Ezzit, Tunisia

University of Sfax, Sfax, Tunisia

Faculty of Computing and IT, University of Jeddah, Jeddah, Saudi Arabia

Keywords:

Heterogeneous Knowledge, Knowledge Harmonization, Knowledge Warehouse, Data Mining, MOT, Meta-

models, Transformation Rules, Source Model, Target Model.

Abstract:

Explicit knowledge extracted from data, formalized tacit knowledge from experts or even knowledge existing

in business sources may be in several heterogeneous formal representations and structures: as rules, models,

functions, etc. However, a knowledge warehouse should solve this structural heterogeneity before storing

knowledge. This requires speciﬁc tasks of harmonizing. This paper ﬁrst presents our proposed deﬁnition and

architecture of a knowledge warehouse, and then presents some languages for knowledge representations as

particular the MOT (Modeling with Object Types) language. In addition, we suggest a metamodel for the

MOT, and a metamodel for the explicit knowledge obtained using decision trees technique. As we aim to

represent knowledge having different modeling formalisms into MOT, as a uniﬁed model, then we suggest

a set of transformation rules that assure the move from the decision tree source model into the MOT target

model. This work is still in progress, it is currently completed with tranformations for additional.

1 INTRODUCTION

In recent years, there has been a growing interest in

knowledge both for personal and professional life.

Thus, many companies have devoted time and efforts

to discover relevant information either on themselves

or touching their customers and competitors.

Hence, knowledge is a power means which helps

companies to improve their decision making process

and to better manage competitive situations. Further-

more, the evolution of the company in its environ-

ment, in addition to its external challenges, pushes it

to capitalize knowledge in order to be more efﬁcient

and then to increase proﬁtability. From this situation

was born the need to gather knowledge into a repos-

itory commonly called knowledge warehouse (KW).

A KW has a speciﬁc architecture providing the neces-

sary infrastructure to extract, store and handle explicit

knowledge (Nemati et al., 2002).

In fact, explicit knowledge existing in the com-

pany and used in decision-making process has ini-

tially heterogeneous formats. In order to put different

types of knowledge together in the KW, we need to

harmonize and homogenize. A means to do this we

have elected the usage of the semi-formal graphical

language for knowledge modeling called MOT (Mod-

eling with Object Types (Paquette, 2002)). The MOT

language relies on a set of typed knowledge units and

links allowing homogeneous modeling of heteroge-

neous explicit knowledge. Our objective is to repre-

sent knowledge having different models from source

models (i.e., decision tree, association rules, cluster-

ing) into the uniﬁed target model which is the MOT.

In this paper we focus on the transformation of deci-

sion tree knowledge into the MOT model.

The rest of this paper is organized as follows: Sec-

tion 2 introduces the context of this work and our def-

inition and architecture for a KW. Section 3 presents

some languages for knowledge representations and

then presents the MOT language and its metamodel.

Section 4 proposes the metamodel for models of ex-

plicit knowledge obtained by decision tree technique.

In addition, it deﬁnes transformation rules from this

metamodel into the MOT metamodel. Finally, sec-

tion 5 concludes the paper and gives some relevant

perspectives.

2 CONTEXT

For business intelligence purposes, we suggest to

gather knowledge (i.e., tacit and explicit knowledge)

616

Ayadi R., Hachaichi Y., Alshomrani S. and Feki J..

Decision Tree Transformation for Knowledge Warehousing.

DOI: 10.5220/0005380506160623

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 616-623

ISBN: 978-989-758-096-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

into a knowledge warehouse (KW) for which we give

our deﬁnition and architecture. Our suggestions rely

on several works of the literature (Kerschberg, 2001),

(Dymond, 2002), (Nemati et al., 2002), (Qing-lan and

Zhi-jun, 2009) and (Irfan and uddin Shaikh, 2010)

that have tried to introduce the KW concept and pro-

pose a basic architecture for a KW. However, these

authors did not provide a precise deﬁnition of a KW

and did not detailed its architecture. Indeed, in their

works, a KW is considered as a data warehouse (Dy-

mond, 2002) (Nemati et al., 2002) described with

three layers: capture, storage and access to content.

For example, (Dymond, 2002) considers that the stor-

age structure is referred to as a knowledge base and

is constructed as a tree with objects at the nodes. Ob-

jects are packages containing data in ”attributes” and

blocks of program code in ”methods”.

In addition, most of proposed architectures are

presented in an abstract way and are composed of

three layers (Data source layer, Knowledge manage-

ment layer and Knowledge presentation layer) (Ker-

schberg, 2001) (Dymond, 2002) (Nemati et al., 2002)

(Irfan and uddin Shaikh, 2010). These architectures i)

do not present the interaction between decision mak-

ers and the KW (i.e., How to exploit and update stored

knowledge?) and ii) do not specify the sources of

knowledge: are they exist in business sources of com-

panies, are they extracted from existing data or are

they acquired through the formulation of tacit knowl-

edge from individuals working in these companies.

Furthermore, in the absence of a complete deﬁni-

tion of KW, we rely on the following extracts so that

we can give our deﬁnition of a KW:

• The knowledge can be found in multiple repos-

itories under multiple heterogeneous representa-

tion (Kerschberg, 2001) such as databases, docu-

ments, computer programs and even in people’s

heads (Dymond, 2002);

• The knowledge extracted and stored in the KW

should be explicit (Kerschberg, 2001) (Nemati

et al., 2002), i.e., formal and systematic in order to

be easily communicated and shared (Nonaka and

Takeuchi, 1995);

• The KW can be used as a clearinghouse of knowl-

edge to be used throughout the organization by the

employees to support their knowledge intensive

decision-making activities (Nemati et al., 2002).

Based on these three extracts that we consider

incomplete and often reﬂecting a particular view-

point, we provide our deﬁnition. In our viewpoint,

a Knowledge Warehouse gathers explicit knowledge

that may come from multiple sources having heteroge-

neous formats and relating to several business activ-

ities within a domain. This knowledge is uniﬁed and

integrated in order to support an intelligent decision-

making process (Ayadi et al., 2013).

Based on this deﬁnition, we draw multi-layer core

architecture for a KW (cf., Figure 1). In this archi-

tecture, we ﬁnd the following tasks located at three

layers (Ayadi et al., 2013).

Data Acquisition: This step is interested in col-

lecting the initial data from companies belonging to

one or more sectors.

Knowledge Extraction: Extracts the hidden

knowledge from the initial data by using multiple

knowledge extraction techniques such as data mining

techniques (Zaki and Meira, 2014).

Tacit Knowledge Explicitness: This step provides

experts with knowledge models to express their tacit

knowledge into explicit knowledge in order to be used

by a computerized decision process.

Knowledge Harmonization: The harmonization

aims to standardize knowledge expressed in hetero-

geneous formats before being loaded into the KW. It

is based on a transformation process that transforms

knowledge from a source model into a common tar-

get model (i.e., MOT).

Knowledge Storage: Storing knowledge accord-

ing to a KW model is crucial for the computerized

decision process. Naturally, stored knowledge could

be later updated by the KW administrator in order to

manage knowledge evolution over time.

Knowledge Exploitation: Once the KW is loaded,

the usage of its content is the ultimate step for intelli-

gently solving decision problems.

As a further step in the KW life cycle is the KW

maintenance. After a decision was taken and evalu-

ated, if the decision maker is satisﬁed then he vali-

dates it, otherwise he can inform the KW administra-

tor to update knowledge that led to invalid decisions.

In the remaining of this paper, we focus on

the knowledge harmonization as a keystone task for

knowledge warehousing. Knowledge has often sev-

eral representation forms: tacit knowledge of experts

or knowledge extracted through data mining tech-

niques. For this reason, we elected the MOT language

as a uniﬁed language for knowledge representation.

3 KNOWLEDGE

REPRESENTATIONS

In the literature, there are several languages for repre-

senting knowledge (Fensel et al., 1994): i) the infor-

mal language allows knowledge explicitness in form

of sentences, their speciﬁcations contain ambiguity

and contradictions and lack precision, ii) the formal

DecisionTreeTransformationforKnowledgeWarehousing

617

Figure 1: Basic architecture for a knowledge warehouse (Ayadi et al., 2013).

language is a set of strings of symbols that may be

constrained by rules that are speciﬁc to it. It is used,

among other things, for the precise deﬁnition of data

formats and the syntax of programming languages.

These languages are hard to understand and it is very

difﬁcult to extract an intuition about the functional-

ity of a system given the huge amount of details of

a formal speciﬁcation only and iii) the semi-formal

language is a knowledge expression formalism whose

grammatical and semantic ﬂexibility makes it user-

friendly (H

eon, 2012). It has advantages compared

to informal language because it provides a represen-

tational guidance which structures the modeling pro-

cess. On the other hand, compared to the formal lan-

guage, the semi-formal language offers greater ease of

use and offers the possibility of expanding the num-

ber of people qualiﬁed to represent their knowledge,

without the help of knowledge engineers.

Since our work aim to model knowledge for deci-

sion makers and due to the advantages of semi-formal

languages we choose using a semi-formal language

that allows a graphical representation of knowledge

and that facilitate the knowledge modeling. In the re-

mainder, we will study the best known semi-formal

languages to choose the most appropriate to our work

(Paquette, 2002).

Semantic Trees are used to categorize concepts

and provide instances for these concepts. The most

general concepts are on top of the tree and the most

speciﬁc concepts are arranged hierarchically below.

The upper links indicate a relationship between a

class and its subclasses while the lower links are links

of belonging of an instance to a class. This type of

graphs leaves several possible interpretations since it

does not specify the nature of the link.

Concept Maps were developed by Joseph D. No-

vak in the 70s and they are also used to represent rela-

tionships between concepts. They also have the form

of a graph, but not necessarily of hierarchical nature.

This time, the nature of each link is speciﬁed by a la-

bel. These maps use non-oriented links that leads to

complicate interpretation. Then, the imprecise choice

of labels can make it difﬁcult the knowledge transfer.

Semantic Networks were invented by Allan

Collins and Ross Quillian in 1969. These networks

are graphical representations analogous to concept

maps but they use oriented links. Semantic networks

use two types of nodes i) nodes representing taxo-

nomic knowledge (concepts and their instances) and

ii) others representing the properties of these con-

cepts. Furthermore, they also employ two types of

links i) structural links (e.g., is-a, has, part-of) and ii)

links speciﬁc to the domain semantic. However, these

networks complain about the lack of standard termi-

nology and standard graphical representation.

MOT (Modeling with Object Types) Language,

2010)) in collaboration with researchers at the re-

search center LICEF

, provides a graphical formal-

ism in order to facilitate the representation of explicit

knowledge of various formats as well as the articula-

tion of experts’ tacit knowledge. The MOT language

allows for a quality gain in knowledge representation,

since it has greater expressiveness to represent pro-

cedural and strategic knowledge other than declara-

tive knowledge (H

eon et al., 2010) (H

eon, 2012). It

enables to represent abstract and factual knowledge

as well as the relationship between them using ori-

ented typed links. Thus, MOT is used to represent

several types of models: facts systems, taxonomies,

procedural systems, methods, processes, multi-agent

Research Center: Laboratory of Cognitive Informatics

and training environments (LICEF) of the Quebec Teleuni-

versity.

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618

systems, etc (Paquette, 2002). Being a language that

overcomes problems of the others semi-formal lan-

guages, we elected the MOT to represent knowledge

in the KW. The next sub-sections describe the alpha-

bet, semantics, grammar and meta-model of MOT.

3.1 The MOT Alphabet, Semantics and

Grammar

The MOT alphabet includes two types of graphical

symbols for knowledge and relationships (i.e., links).

It allows representing knowledge by geometrical ﬁg-

ures that differentiate their types and their abstraction

levels (cf., ﬁgure 2):

Figure 2: MOT shapes used for knowledge representation.

• Abstract Knowledge: knowledge that represent

classes of objects (Paquette, 2002). There are

three types of abstract knowledge: Concept, Pro-

cedure and Principle;

• Factual Knowledge (Facts): refers to a tangi-

ble entity to describe concrete objects (Paquette,

2002) (H

eon, 2011). There are three types of

facts: Example, Trace and Statement.

From the semantic viewpoint, different types of

abstract and factual knowledge are expressed as fol-

lows (Paquette, 2002) (H

eon, 2012):

• Concept (declarative/conceptual knowledge) rep-

resents a class of objects. Indeed, it is the abstrac-

tion of a concrete object called Example;

• Procedure (procedural knowledge) describes the

sets of operations for acting on the objects. The

instantiation of a procedure gives a Trace;

• Principle (strategic knowledge) makes causal

links between objects, determining conditions that

may apply to the implementation of actions and

representing agents that act on something. Instan-

tiating a principle gives a Statement.

Relationships are represented by directional links

between knowledge. MOT covers a set of seven typed

links (Paquette, 2002) (H

eon et al., 2010):

• Link I: Instantiation link connects an abstract

knowledge to a fact;

• Link C: Composition link connects knowledge to

one of its components or its constituent parts;

• Link S: Specialization link connects two abstract

knowledge of the same type, one of which is a-

kind-of the other;

• Link P: Precedence link associates two procedu-

ral knowledge or strategic knowledge such as the

ﬁrst must be completed and evaluated before the

second starts;

• Link I/P: Input/Product link used to associate a

procedural knowledge and a conceptual knowl-

edge in order to represent the input or the product

of procedural knowledge;

• Link R: Regulation link from strategic knowledge

to another knowledge speciﬁes a constraint, re-

striction or rule that governs knowledge;

• Link A: Application link is an association between

two domains: the ﬁrst domain plays the role of

meta-knowledge for the second. Thus, the link A

allows the combination of a fact to a knowledge.

In addition to rules of good practices, the MOT

language offers integrity rules for each type of link

(cf., Table 1) (Paquette, 2002) (H

eon et al., 2010)

eon, 2011) . These rules deﬁne the valid relation-

ships between different types of knowledge.

3.2 The MOT Metamodel

Recall that our goal is the representation of explicit

knowledge expressed in heterogeneous formats into

the MOT language; we propose the metamodel of this

language (cf., Figure 3) based on its descriptions in

(Paquette, 2002). The MOT model is composed of n

(n≥0) Links and m (m≥0) Knowledge unit(s) (KU).

Each KU has a name name KU and can be either

an Abstract knowledge unit (AKU) either a Factual

knowledge unit (FKU). In turn, an AKU can be a Con-

cept, Procedure or Principle. Each AKU can be in-

stantiated to many FKU which can be an Example,

Trace or Statement. In MOT, a Link can be of type {I,

C, P, S, I/P, R, A}. It links a KU source to a KU desti-

nation. we note that the Link C may have a cardinality

represented as a value written in the arrow head and

which is 1 by default.

To meet our needs and as we aspire that the MOT

meta-model covers all types of explicit knowledge,

we extend its meta-model by adding two new classes

called Position and Principleprocedure depicted in

red color in Figure 3. The class Position has the

attribute number which represents the position of a

The link R between a concept and a principle is an Add

in (H

eon et al., 2010) (H

eon, 2011) (H

eon, 2012). It is not

part of the original grammar MOT proposed by Paquette

(Paquette, 2002).

DecisionTreeTransformationforKnowledgeWarehousing

619

Table 1: Integrity rules for MOT links.

Destination Abstract knowledge Factual knowledge

Origin Concept Procedure Principle Example Trace Statement

Concept C, S I/P R

I, C

Procedure I/P C, S, P C, P I, C

Principle R C, R, P C, S, P, R I, C

Example A A A A,C A,I/P A

Trace A A A A, I/P A, C, P A, C, P

Statement A A A A, R A, C, R, P A, C, R, P

Figure 3: The extended MOT metamodel.

KU. This class is useful when deﬁning transformation

rules from the decision tree metamodel where each

decision node of the tree has a position (cf., section

4.1). The class Principleprocedure represents a KU

that can be at the same time a Principle and Procedure.

This new AKU will be useful to transform knowl-

edge from the association rules model into the MOT

model. In this MOT metamodel, we have taken into

account transformations for three models of the three

most used techniques (ie, decision trees, association

rules, clustering). Note that we are working on how

to integrate knowledge coming from other data min-

ing techniques (e.g., neural networks, support vector

machines), therefore, the presented meta-model can

be extended by incorporating more concepts (i.e., at-

tributes, classes).

Our objective is to deﬁne transformation rules that

assure the move from a decision tree source model

into the MOT target model. Instead of deﬁning the

rules at the model level we position ourselves at a

higher abstraction level so then we deﬁne rules at

the metamodel level (MM); thus we gain genericity

of rules. Indeed, our transformation rules associate

a concept of the source MM with its corresponding

concept in the target MM (Van Sinderen et al., 2012).

In the next section we limit ourselves to present trans-

formation rules from the decision tree MM.

4 DECISION TREE TECHNIQUE

In this section we are interested in decision tree as a

knowledge source and its transformation into MOT.

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620

4.1 Decision Tree Metamodel

Figure 4 presents the decision tree metamodel (DT-

MM). In this MM, a Decision tree is composed of De-

cision node(s), Branch(es) and Leaves. Each Decision

node has a name name DN and a position positionN.

Each Branch is a path for a possible decision or oc-

currence and is labeled with a value. Each Leaf has

a position positionF and represents the class label of

the object to be classiﬁed. This class can be of type

Conclusion or Action. Each Decision node receives

a single Branch except the root node, and may have

several outgoing Branches. Each Branch associates

with its source one Decision node and with its desti-

nation either a Decision node or a Leaf. A Leaf is the

destination of a single Branch.

Figure 4: Decision tree metamodel (DT-MM).

Figure 5 shows a sample decision tree built on

a set of 14 observations to forecast the behavior of

sports described with 5 attributes (Quinlan, 1993), by

using the decision tree construction technique in data

mining (Althuwaynee et al., 2014). Indeed, the be-

havior of sports is predicted by determining the Play

attribute (represents the leaf of the DT having the yes

or no class). This prediction is based on the weather

data (Outlook of the sky, Temperature, Humidity and

Wind) representing decision nodes of the DT.

Figure 5: A sample decision tree.

4.2 Transformation Rules from DT to

MOT

The transformation rules link between the DT-MM el-

ements and those of the MOT metamodel. Let’s note

that some elements of the DT-MM transforms to AKU

(e.g., principle, procedure) whereas others transforms

to links (e.g., link P, link C) between knowledge units.

Thus, we distinguish two types of rules.

4.2.1 Transformation Rules to Generate

Abstract Knowledge Units (AKU)

In the following transformation rules the term root of

a decision tree (DT) refers the Decision node located

at position zero (highest level).

RD1. The root R of a DT becomes an AKU of type

Principle Pr1. The name of Pr1 is R; its position

is that of R;

RD2. The value of a branch Br1 (n1, n2); i.e., a

branch from node n1 to node n2, of a DT becomes

an AKU of type Principle Pr2. The name of Pr2 is

the concatenation of n1 (name of the source node)

with the value of Br1. The position of Pr2 is that

of the destination node n2 (decision node or leaf)

of Br1;

RD3. A leaf F1 in a DT transforms into i) an AKU

of type Principle Pr3 when the class of F1 is a

Conclusion, and ii) to an AKU of type Procedure

Proc when the class of F1 is an Action. The name

of Pr3 or Proc is the class of the leaf F1. The

position of Pr3 or Proc is positionF of the leaf F1

concatenated with ”.1”.

Note that in this rule, the type of the resulted AKU

(i.e., Principle or Procedure) depends on the seman-

tics of the leaf class F1 (i.e., Conclusion or Action).

4.2.2 Rules for Linking AKU

In a decision tree modeled in MOT each AKU-node

(i.e., Principle or Procedure) is spotted by its posi-

tion. The root has position 0 (zero), its immediate

descendants are numbered sequentially from left to

right including their ancestor number (zero) starting

from 0.1. Further descendants are numbered relative

to their ancestors as 0.1.2.3 for the third descendant

of the second child of the ﬁrst descendant of the root

(cf., Figure 6).

Figure 6: Position of AKU (Abstract Knowledge Unit).

In order to link all AKU-nodes obtained by rules

RD1 to RD3 we rely on their positions as stated in

rule RD4.

RD4. Two AKU n1 and n2 having their positions x

and x.i respectively will be related by a link from

n1 to n2; where i is a single digit. As an exam-

ple, if x is 0.1.2 (position of n1) and n2 position is

DecisionTreeTransformationforKnowledgeWarehousing

621

0.1.2.4 then a link is constructed starting from n1

towards n2. The type of the link being constructed

is determined by the following two rules:

RD4.1. If n1 is a principle Pr1 issued from rule

RD1 and n2 is a principle Pr2 (obtained with

rule RD2) then their link is of type link-C and

has the default cardinality (one);

RD4.2. If n1 is a principle Pr2 issued from RD2

and n2 is an AKU issued from rule RD2 or RD3

then the type of their link is link-P.

In the next section we develop the DT2MOT algo-

rithm based on the four above rules and we present its

application on the example of ﬁgure 5.

4.3 The DT2MOT Algorithm

In this section we develop the DT2MOT algorithm

that transforms a DT to MOT. This algorithm is not

optimized; it shows step-by-step the transformation

of a given DT to MOT and indicates in comments of

each method the used transformation rule (cf., Algo-

rithm 1). Figure 7 shows AKU obtained from the DT

of ﬁgure 5 by applying the DT2MOT algorithm from

line 3 to line 13. In this ﬁgure, the position and the

rule of each extracted AKU are depicted.

Figure 7: AKU and their positions (obtained from tree of

ﬁgure 5).

Figure 8 shows the MOT model obtained from the

DT of ﬁgure 5. In this model, AKU of ﬁgure 7 are

linked by applying the DT2MOT algorithm from line

14 to line 19.

Algorithm 1: DT2MOT algorithm.

Input: Decision Tree DT

Output: MOT model

1 Declaration: j ∈ N*, i is a position of a KU

conform to notation used in RD4

2 begin

3 foreach decision node dn ∈ DT do

4 if (positionN of dn = 0) then

/* node located at position

zero is the Root */

5 Transform root to principle (dn,

Pr1) /* this method

corresponds to rule RD1

where dn and Pr1 are

respectively the input and

the output */

6 Break;

7 foreach branch br ∈ DT do

8 Transform branch to principle (br, Pr2)

/* rule RD2; br: input and

Pr2: output */

9 foreach leaf f ∈ DT do

10 if (class of f is a Conclusion) then

11 Transform leaf to principle (f, Pr3)

/* rule RD3.i; f: input

and Pr3: output */

12 else

/* class of f is Action */

13 Transform leaf to procedure (f,

Proc) /* rule RD3.ii; f:

input and Proc: output

/* Note that the calls for the four

previous methods could be

executed in parallel, they

generate AKU with their

positions */

/* Linking the AKU task */

14 foreach AKU n1 do

15 foreach AKU n2 6= n1 do

16 if (position of n1 = 0 and position of

n2 = 0.j) then

17 Link by linkC (n1, n2) /* rule

RD4.1; n1: source of

linkC and n2:

destination of linkC */

18 else if (position of n1 = i and

position of n2 = i.j) then

19 Link by linkP (n1, n2) /* rule

RD4.2; n1: source

and n2: destination

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622

Figure 8: Decision tree of ﬁgure 5 transformed into MOT.

5 CONCLUSION AND

PERSPECTIVES

In order to load explicit knowledge in the knowledge

Warehouse (KW) we need to unify their modeling and

solve structural heterogeneity. To represent knowl-

edge having different models into a uniﬁed one, we

have elected the semi-formal language for graphical

knowledge representation called Modeling with Ob-

ject Types (MOT) for which we have designed its

metamodel. In addition, we have deﬁned in this pa-

per the Decision Tree (DT) metamodel and we have

suggested a set of appropriate rules that allows trans-

forming DT model into MOT model. Furthermore,

we have developed an algorithm describing the prin-

ciple of these transformations that we have illustrated

with an example.

We are working on several extensions: how to

transform into MOT language, other kinds of ex-

tracted knowledge by using other techniques such as

associations rules, ECA rules, non-monotonic knowl-

edge, clustering, neural networks, support vector ma-

chines, etc. Then, how to integrate these many MOT

models produced from different source models (e.g.,

Decision tree, association rules, clustering) and what

are the operators that should be used to query and

maintain an MOT knowledge Warehouse. Using the

KW during an intelligent decision-making process is

the ultimate objective of this research.

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