BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE
FROM EXPLICIT DATA
Considering Administrative Management Processes
Anna Rozeva
Department of Business Management, University of Forestry, 10 Kliment Ohridski blv., Sofia, Bulgaria
Martin Ivanov
New Bulgarian University, 21 Montevideo str., Sofia, Bulgaria
Roumiana Tsankova
Department of Management, Technical University, 8 Kliment Ohridski blv., Sofia, Bulgaria
Keywords: Knowledge generation, Business model, Text mining, Data mining, Administration management,
Categorization, Clustering.
Abstract: The aim of the paper is to present a framework for designing business model for knowledge generation from
explicit data on “good” administrative management practices. Knowledge discovery demands the
availability and access to high volumes of data. There is such data collected in databases and files in
different formats. Knowledge extraction is performed by statistical and machine learning mining methods of
text mining and data mining. The proposed framework for business model design consists of structure and
knowledge models. The two models refer to the text transformation and the knowledge extraction phases
respectively. Structure model implements text mining methods for converting the text documents into
structured objects. These objects form a data mining structure that is the source for the knowledge discovery
models. They are oriented to descriptive and predictive modelling tasks which concern document clustering
and categorization. The business model framework is trained on source text documents for “good” practices
in public administration and business management which are classified according to preliminary established
topics. The results obtained by the descriptive and predictive knowledge models are presented.
1 INTRODUCTION
Administration management processes generate
continuously growing amount of data in a variety of
digital formats. Part of it is structured in databases
but there is a significant volume that is unstructured,
i.e. plain text documents, images, sound and videos.
This data is referred to as explicit. Structured data is
processed for extracting business valuable
information by means of standard database queries.
At the same time the information contained in
unstructured documents cannot be exposed by
traditional data processing techniques. The explicit
data contained especially in text documents
represent a natural pool from which information of
high quality can be obtained. The higher quality is
provided by the information that isn’t explicitly
stated but is “hidden”, “buried” or “derivable” from
the data contained in the documents concerning
administration management digital collections.
The information that is derivable and uncovered
from digital data stores is considered knowledge.
When extracted by implementing the proper
information technology it turns into valuable asset
for administration and business management that
raises its quality and competitiveness. The generated
knowledge is already in a structured form. It can be
stored, processed, managed, combined and involved
in further knowledge generation processes. The
main challenge therefore is to turn the available
unstructured digital data that is in a variety of
114
Rozeva A., Ivanov M. and Tsankova R.
BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE FROM EXPLICIT DATAConsidering Administrative Management Processes.
DOI: 10.5220/0004459201140121
In Proceedings of the First International Symposium on Business Modeling and Software Design (BMSD 2011), pages 114-121
ISBN: 978-989-8425-68-3
Copyright
c
 2011 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

formats into numerical form that can be processed
by standardized database techniques. The process to
achieve this is text mining. The knowledge exaction
by itself is performed by “mining” technique which
is referred to as data mining. The aim of the paper is
to design an approach for mining unstructured
digital data documents on “good” practices for
administration management resulting in extracted
knowledge. The remainder of the paper is organized
as follows: The second section is a review on mining
approaches and results obtained. The third section
presents a business model for knowledge generation
from a data pool. The fourth section describes case
study on documents concerning “good” practices in
administration management and application of the
model in selected platform for knowledge extraction.
The last section concludes with discussion on the
results obtained.
2 MOTIVATION FOR
KNOWLEDGE GENERATION
Text data sourced from the World Wide Web,
governmental and municipal digital repositories,
blogs, e-mails, news, papers, articles, etc represent a
data pool for analytical processing. Analysis of text
resources performed by text mining technique is
discussed in (Stavrianou and Anrditsos, 2007). It’s
considered to involve the following steps: parsing,
pattern recognition, syntactic and semantic analysis,
clustering, tokenization, application of statistics and
machine learning algorithms. The analysis results
are evaluated for emerging the previously unknown
knowledge. It’s used further on for database
population or reconciliation.
Filtering and ranking of search results from
bibliographic databases is considered in (Faulstich
and Stadler, 2003). Text classifiers for filtering as
automated text categorization and calculation of
probabilities as document scores are implemented.
The establishment of the training data sets from
articles as PDF documents, data preparation for
conversion into text and further conversion into
vector representation are considered. The application
for document classification with a variety of
classifiers from WEKA (Witten and Frank, 2011) is
presented.
A text mining system for knowledge discovery is
presented in (Uramoto and Matsuzava, 2004). It
represents an extension of a commercial mining
system for a biomedical document base. It treats the
information extraction and entity/relationship
mining by means of domain dictionary. Public
ontological knowledge is used for constructing
categories from extracted entities and relationships
among them. Relationships are of type noun and a
verb and two nouns and a verb. The information
extraction process involves finding words by using a
term dictionary. The obtained words are embedded
in the text document as annotations in XML. The
annotated text is passed to a syntactic parser. It
outputs segments of phrases labelled with their
syntactic roles, i.e. noun phrase or verb group.
Further on categories are assigned to the terms in the
segments and phrases. Mining functions are
provided for discovering underlying information.
In order to analyze plain text documents it’s
necessary to pre-process them for obtaining data
structure that is more applicable for mining with
available statistical and machine learning methods.
Traditional data mining (Nisbet and Elder, 2009)
involves algorithms that are applied to structured
data. Consequently in order to implement them to
plain text documents text pre-processing is to be
performed. It involves syntactic and semantic
analyses of the text. For the purposes of both
analyses the approach is to identify the words in the
documents. And as a result the document is
represented as a set (bag) of these words. Other
approaches consider the importance of the words in
a document which is measured by numerical value.
A document is therefore represented as a vector in a
multidimensional space with measures for the words
contained therein. The resultant representation is
structured and suitable for processing with data
mining techniques. Basic text mining methods are
presented in (Hotho and Nurnberger, 2005). They
refer to either assigning keywords to documents
based on a given keyword set (classification or
categorization) or automatically finding groups of
similar documents (clustering).
Another aspect of the knowledge generation
process performed by text mining techniques refers
to knowledge sharing. It requires linking of
knowledge to ontologies as main repositories of
formally represented knowledge. As shown in
(Spasic and Ananiadou, 2005), ontologies provide
the framework for the semantic representation of
text documents. They serve for mapping terms
extracted from documents to domain-specific
concepts. By implementing them text can be mined
for interpretable information and not only to
discovering simple correlations based on co-
occurrences of targeted classes of terms.
Architecture for discovering conceptual structures
and generation of ontologies is presented in
BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE FROM EXPLICIT DATA - Considering
Administrative Management Processes
115

(Maedche and Staab, 2000). Text processing
performs syntactic analysis of natural language
documents. It involves word extraction
(tokenization), lexical analysis and grammatical
parser.
The phases of the text mining process after
(Castellano and Mastronardi, 2007) are shown in
Figure1.
Figure 1: Text mining lifecycle.
The text mining phases are document clustering,
document categorization and pattern extraction.
The review of research on knowledge generation
from text documents motivates our work on
establishing a business model for analysis resulting
in extracted knowledge.
3 BUSINESS MODEL FOR
KNOWLEDGE GENERATION
Architecture for knowledge generation system from
digital document collection is presented in
(Tsankova and Rozeva, 2011). The concept is the
knowledge generation model to be comprised of text
mining model and data mining model. The
motivation is to separate the phase concerning the
conversion of text documents into representation
that is suitable for processing through data mining
techniques and the real knowledge generation phase.
Current work is further enhancement of the
knowledge generation system design which concerns
the mining model design. The proposed general
model architecture is shown in Figure 2. The
structuring phase produces text database from the
documents by imposing a column/row structure. The
least number of columns is two, one for the
document identifier and the other for the document
text. The text being placed in a column becomes
available to text mining tools. The text mining tool
deals with text encoding and generates the structure
model. It represents the mining structure as a source
for the models that generate the knowledge.
Figure 2: Knowledge generation model.
Each of the components is obtained as a result of
a process that comprises several steps.
3.1 Mining Structure Design
The document pool is assumed as a set of plain text
documents. The initial document format and the
conversion to plain text aren’t considered in the
knowledge generation framework. Text structuring
can be performed by ETL tool and imported in a
database table.
3.1.1 Document Term Representation
The structure model generation deals with
identification of words in the text. A way for
designing it consists of the following steps:
 Tokenization – obtaining stream of words from
documents and combining the different words into
dictionary;
 Dictionary reduction – filtering, stemming;
 Term selection – further reduction of the
dictionary by removing words based on calculating
their entropy or another method.
Standard method of filtering the dictionary for
reducing its size is stop word removal. Stop words
bear little or no information content and also occur
either extremely often or very seldom. The often
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occurrence doesn’t provide for distinguishing
between documents. The seldom occurrence is of no
particular statistical relevance.
Stemming methods produce basic word forms
and hence groups of words with equal or similar
meaning.
Term selection removes the words that are not
suitable for separation of documents when searched
by keywords.
The approach that has been selected for
designing document structure model is document
vector space. A document is represented as a vector
with elements being terms, i.e. words or phrases.
The vectors’ size is determined by the number of
words in the document collection. The selected
document encoding is binary. The term vector
element is set to 1 if the term is encountered in the
document and 0 otherwise. In this representation
terms are considered equally important. In order to
take into account the term importance for describing
a document, weighting scheme is applied. Several
weighting schemes are shown in (Hotho and
Nurnberger, 2005). The weighting scheme selected
for the structure model represents the product of the
term frequency tf(d,t) and the inverse document
frequency idf(t)=log(N/n
t
). The inverse document
frequency takes into account the size of the
document collection N and the number of documents
that contain the term n
t
. The term weighting scheme
that is chosen implements length normalization
factor as well. It eliminates the influence of
document length on the chance for retrieving it. The
term weight is computed after equation (1):
∑
=
=
m
j
n
j
t
N
td
j
tf
n
t
N
tdtf
tdw
1
2
2
)log(
)
,
(
)log(),(
),(
(1)
By implementing the weighting scheme a document
in the vector space is represented by the term
weights as (2):
)),(),...,,(()(
1 m
tdwtdwdw =
(2)
The term weight representation allows for defining
similarity between documents by means of the inner
product of their vectors (3):
),().,(),(
1
1121
∑
=
=
m
k
kk
tdwtdwddS
(3)
The distance between the two vectors is calculated
by Euclidean distance (4):
∑
=
−=
m
k
kk
tdwtdwdddist
1
2
2121
),(),(),(
(4)
3.1.2 Linguistic Document Processing
Linguistic processing enhances document term
representation by attaching labels for describing
them as parts of speech, grouping them into
sentences and eliminating disambiguities. The
structure model’s design involves the following
linguistic processing steps:
 Assigning tags for part of speech to each term –
noun, verb, adjective;
 Text chunking – grouping adjacent words into
sentences;
 Determination of word sense – the semantics of
words in the specific context is determined;
 Grammatical parsing – discovering the relation of
each word to the others as well as its function in the
sentence, i.e. (subject, object).
Linguistic processing is implemented by means of
ontologies as shown in (Amardeilh and Laublet,
2005). The structure model from Figure 2 supports
part of speech tagging. So far as the determination of
word sense is considered the vector representation
provides for automatic disambiguation by evaluation
of term co-occurrence.
The process flow diagram for designing the
document structure model is shown in Figure 3.
Figure 3: Document structure model design.
3.2 Knowledge Models Design
The document structure model represents structured
database objects. Database objects comprise the
dictionary of the document corpus and the document
vectors. The structure model provides the source
BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE FROM EXPLICIT DATA - Considering
Administrative Management Processes
117

data for designing data mining models for
knowledge generation. Data mining methods and
algorithms for building complex and powerful
knowledge generation models are presented in
(Larose, 2006) and in (Cios and Pedrycz, 2000).
The knowledge model consists of the mining
structure and the mining algorithm. The algorithm to
implement on the mining structure depends on the
mining task. Hand and Mannila (2001) define the
basic mining tasks as descriptive modelling and
predictive modelling. The knowledge models in the
knowledge generation framework from Figure 2 that
perform these tasks are denoted as DM and PM.
The aim of descriptive modelling is to find
models for the data. They are implemented in the
setting of unsupervised learning. Typical methods of
descriptive modelling are data segmentation and
clustering. The reasoning behind the cluster analysis
is that there are natural clusters in the data and the
mining task consists in uncovering and labelling
them.
Predictive modelling represents supervised
learning. There is a target variable which has to be
presented as function of other variables. The nature
of the target variable determines the type of the
model. Classification model applies to discrete
variable and regression model – to continuous
variable. Approaches for classification models are:
 Discriminative;
 Regression;
 Class-conditional.
The discriminative approach performs direct
mapping of input variables to one of k possible
target categories. The input space is partitioned into
different regions which have a unique class label
assigned. Examples of this approach are the neural
networks and support vector machines.
The regression approach determines the posterior
class distribution for each case and chooses the class
for which the maximum probability is reached.
Decision trees (CART, C5.0, CHAID) classify for
both the discriminative approach and the regression
approach, because typically the posterior class
probabilities at each leaf are calculated as well as the
predicted class.
The class-conditional approach starts with
explicit specification of class-conditional
distributions. After estimating the marginal
distribution, Bayes rule is used to derive the
conditional distribution.
The proposed knowledge generation model
involves clustering descriptive model and
categorization predictive model. The models’ design
will be presented further on with the architecture and
the implemented algorithm.
3.2.1 Descriptive Model (DM) Architecture
The descriptive model is designed as clustering
model. It will produce groups with documents that
are more similar than those in the other groups.
Clustering is performed after the hypothesis that
relevant documents tend to be more closely related
to one another than to non-relevant documents. The
clustering algorithm that is implemented is the k-
means algorithm. It performs distance-based flat
clustering as follows:
Input: D{d1,d2,…dn }; k-the cluster
number;
Select k document vectors as the
initial centriods of k clusters
Repeat
Select one vector d in remaining
documents
Compute similarities between d and k
centroids
Put d in the closest cluster and
recompute the centroid
Until the centroids don’t change
Output:k clusters of documents
3.2.2 Predictive Model (PM) Architecture
The predictive model is designed as classification
model since term weights in the document vectors
are discrete. Classification task consists in assigning
one or more predefined categories (topic themes) to
a document.
Mining structure in the architecture that is
provided by the structure model is document vector
space. The new document for classification is turned
into vector representation by passing it through the
structure model. The vector space model together
with the predefined topics is provided to the
classifier the output of it being the predicted
category for the input document.
The predictive model architecture is
implemented with the Bayes classifier. The
classifier’s algorithm is the following:
Input: document d
Predefined topics T={t
1
, …, t
n
}
Compute probability of d for t
j
∈T
∑∏
∏
==
∈=
=
T
k
t
d
j
kkj
d
j
iji
ii
ttw
twt
d
ttd
i
dt
1
1
)Pr())Pr((
)Pr()Pr(
)Pr(
)Pr()Pr(
)Pr(
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Output: category c assigned to d with
probability Pr
))(Pr(max)Pr( tdtd
Tt∈
=
The classification architecture is shown in Figure 4.
Figure 4: Categorization architecture.
4 KNOWLEDGE GENERATION
MODEL IMPLEMENTATION
The knowledge generation model is implemented on
document corpus of “good” practices for
administration management. Documents on “good”
practices are uploaded on web site and stored in a
digital text database. There are topics defined for
preliminary categorization of the submitted
documents. The topics concerning administration
management are the following:
 National administration and subsidiaries;
 Municipal administrations;
 E-solutions for effective administrative actions;
 Public- private partnership.
The topic national administration involves public
policies, human resource management, new vision
and performance evaluation. The municipal topic
covers decentralization, policies and services. E-
solutions address conceptual framework, e-
government and e-community. And public-private
topic involves national and municipal practices and
solutions. The digital text database stores conference
and workshop presentations and papers.
Since the uploaded documents are categorized
after predefined topics they are suitable to be used in
the predictive knowledge generation model for
performing classification task. The available
documents will be implemented for training the
model. It will be used further on for classifying
newly uploaded documents on good practices. Thus
the good practices digital store will hold
automatically classified documents according to the
stated topics.
The conference and workshop materials in the
digital store provide for the descriptive knowledge
generation model. The model will establish groups
with similar documents. Thus the document corpus
will be structured and the groups defined can be
implemented further on in other mining tasks.
The test document pool for designing and
training the knowledge models consists of 45
documents. The text represents paper titles and
abstracts. The knowledge generation models are
implemented with the WEKA open source data
mining software (Witten and Frank, 2011) and (Hall
and Frank, 2009). The text documents are structured
by converting to the .arff format. The structured file
for the descriptive model task has 2 attributes, i.e.
filename and content. For the predictive model task
third attribute is added for the predefined document
topic (class).
4.1 Document Structure Model
The document corpus structure model is obtained as
follows:
 Term extraction – Alphabetic tokenizer;
 Filtering – stoplist words as stoplist.txt file;
 Stemming – IteratedLovinStemmer;
 Term weighting – TFIDF transformation;
 Part of speech tagging – none;
The resulting vector space structure model is shown
in Figure 5.
Figure 5: Document structure model.
The model contains the document vectors by
rows, terms by columns and term weights by cells.
The structure model represents the mining
structure for building the knowledge generation
models.
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4.2 Descriptive Model Implementation
The descriptive model will elaborate clusters with si-
milar documents. There aren’t predefined topics.
The mining structure obtained is to be appended
with selected term set that is descriptive of the
document corpus content. Selection of descriptive
attributes is performed by means of latent semantic
analysis. It results in attribute set where attributes
are obtained as a liner combination of the initially
extracted terms. The selected latent attributes for
clustering ranked by weight and filtered by
AtttributeSelection filter produce 3 clusters that are
shown in Figure 6.
Figure 6: Clusters of selected terms.
The descriptive model is built with simple k-
means clustering algorithm. The resultant clusters
are shown in Figure 7.
Figure 7: Cluster content.
Clusters’ visualisation is shown in Figure 8.
Figure 8: Cluster visualisation.
4.3 Predictive Model Implementation
Term selection has to be performed for designing the
predictive classification model. The result is a set of
descriptive attributes. Term selection is performed
with the following settings:
 Attribute evaluator - CfsSubsetEval;
 Search method - GreedyStepwise.
The resultant term set for implemented in the
classification model is shown in Figure 9.
Figure 9: Descriptive terms selection.
The predefined topics are encoded with A, B and
C for national and municipal administration and e-
solutions respectively. The classification model is
built with Naïve Bayes classifier. The result is
shown in Figure 10:
Figure 10: Predictive classification model.
The model precision is presented by correctly
and incorrectly classified instances and as confusion
matrix. The tree like view of the classification model
is shown in Figure 11.
Figure 11: Classification model tree view.
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5 CONCLUSIONS
Knowledge based management of administration
and of business provides for enhanced competitive
advantage and quality of services and business
process flow. Practically knowledge doesn’t exist in
explicit form. It’s hidden within data pools of
different format and volume. The task of uncovering
it poses significant challenge to information
technologies and business modelling. Current paper
contributes to the design and architecture of
knowledge generation system with issues concerning
data modelling and implementation for knowledge
discovery. General model for mining knowledge
from digital text stores is presented. The model
framework involves structure model and knowledge
models. The steps for designing them implement
text and data mining techniques. Basic architecture
and algorithms for performing descriptive
(clustering) and predictive (classification) modelling
tasks are presented. The knowledge generation
model is trained on test document corpus for “good”
practices for administration management. The
models are established in WEKA and produce
knowledge results for the clustering and
categorisation tasks. Future work is intended in
extracting associations between terms and
implementation of ontologies.
ACKNOWLEDGEMENTS
The paper presents results of the project “Research
and Education Centre for e-Governance” funded by
the Ministry of Education in Bulgaria.
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