University’s Scientific Resources Processing in Knowledge
Management Systems
Zhomartkyzy Gulnaz
1
, Milosz Marek
2
and Balova Tatiana
1
1
Department of Information System, D. Serikbayev East Kazakhstan State Technical University,
69 А.К. Protozanov, Ust-Kamenogorsk, Kazakhstan
2
Institute of Computer Science, Lublin University of Technology, 36b Nadbystrzycka, Lublin, Poland
Keywords: Knowledge Management System, Ontology, Semantic Search, Terminological Collocations, Collocations.
Abstract: This article deals with some issues of modern approaches to word processing in knowledge management
systems. The method of documents’ profiles formation based on scientific knowledge ontology model
which provides the semantic processing and retrieval of information is proposed. The article describes the
main stages of the university's information resources word processing to form a semantic document profile:
the extraction of terminological collocations, the automatic classification of texts on scientific topics, the
formation of a document’s semantic profile.
1 INTRODUCTION
The development of information technologies has
enhanced the dissemination and the use of
knowledge in all spheres of public life. The
informatization of the society has served the
establishment of knowledge-based economy;
therefore, the research in the field of knowledge
formalization and smart information processing is of
particular importance and practical necessity.
The scientific knowledge of a university is
important for both the university’s innovative
development and its educational process
improvement. The task of preserving and sharing the
knowledge accumulated by the university has been
brought into the forefront, and the research related to
the formation and the completion of the university’s
scientific knowledge base has become topical.
The members of the scientific community need
to interact with each other and to gain access to
information in order to create new knowledge. The
need for information is growing exponentially; the
existing information systems do not provide the
search, development, exchange and management of
information to the full extent. So, the need to
develop knowledge management systems (Maier,
2007) has emerged.
Scientific cooperation is done through paper and
electronic information flows and is based on direct
(personal) communication. Scientific on-line
journals, electronic catalogs and university libraries
databases, the Internet, collaboration tools, and e-
mail facilitate the access to the required information
and accelerate the traditional processes of
knowledge dissemination, but they do not support
the semantic search. Modern smart information
technologies require new scientific approaches and
fundamental changes rather than the automation and
acceleration of traditional information exchange
processes.
The Scientific Knowledge Management System
(SKMS) presented in this paper serves as the
technological component of the university
management; it ensures the creation, organization
and dissemination of scientific knowledge among
the university staff. The ontology is used as the
information model in the SKMS. An ontology is a
conceptual domain model which is the system of
concepts, their properties and relations
understandable to both humans and computers.
The developed ontology of scientific activities
describes the structure of scientific knowledge base,
provides access to its content and allows forming of
employees’ scientific profiles. As a result, when
there is a need in knowledge, a user doesn’t have to
search through different sources. The ontology
allows access to all the necessary knowledge
through a single (user’s) interface regardless of the
knowledge representation form.
225
Gulnaz Z., Marek M. and Tatiana B..
University’s Scientific Resources Processing in Knowledge Management Systems.
DOI: 10.5220/0004886802250232
In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 225-232
ISBN: 978-989-758-028-4
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
The accumulation of scientific knowledge is
provided from different sources of structured
information (digital library databases) and
unstructured information (research articles,
conference proceedings). The unstructured scientific
resources are classified by means of classifiers,
then
they are recorded in the ontology as "Information
resources" class instances and form the document’s
semantic profile.
The aim of this work is to create documents’
profiles in ontology model for effective knowledge
management in scientific knowledge management
system SKMS.
The proposed semantic technologies form the
basis of the university’s scientific knowledge
semantic portal.
2 LITERATURE REVIEW
The ontological approach for annotating documents
for automatic identification of employees’
competences is considered in the papers (Allemang
and Hendler, 2011); (Altınçay and Erenel, 2010).
The paper (Ma et al., 2012) shows the use of
ontology-oriented hybrid methods of TextMining for
processing text applications.
The methods of users’ profiles formation based
on their personal documents and computing the
semantic similarity of users’ profiles with spreading
activation networks derived from ontologies is
described in the paper (Thiagarajan et al., 2008).
The technology for automatic text categorization
for automatic conceptual indexing is described in
literature (Lukashevich, 2011), as well.
This paper describes the methods and the
problems of automatic categorization, the scheme of
heading description, various machine learning
techniques for text categorization. The problems of
document classification and clustering were
described in detail in papers (Manning et al., 2009);
(Du and Chen, 2013); (Shengyi et al., 2012); (Jiang
et al., 2012) in which it was proposed to use the
ontological vocabulary domain. The methods of
terminological collocation automatic extraction in
order to form a domain subset of terms were
outlined in (Pivovarova and Yagunova, 2010);
(Sedova and Kvyatkovskaya, 2011). The topic-
subtopic relations are used in the domain ontology.
The analysis of the available literature sources
confirms the scientific and practical significance of
this document profile formation method based on the
scientific knowledge ontology model, which
provides the semantic processing and retrieval of
information.
3 THE INFORMATION MODEL
OF THE UNIVERSITY’S
SCIENTIFIC RESOURCES
The ontology-oriented approach allows organizing
and structuring of the university's research related
information resources and developing the methods
of search for knowledge.
The information model of the university’s
knowledge can be described as the ontology which
includes the basic concepts of the university’s
scientific activities, such as organizational structure,
subjects, the objects of scientific schools and
research, information resources, other
subdisciplines, etc.
Subclasses correspond to major research areas in
the ontology of scientific activity in the "Research
directions" class. The subclasses consist of topics
which correspond to the headings of the knowledge
area classifier.
OWL DL (Web Ontology Language) is used as
the ontology description language (Allemang and
Hendler, 2011). The developed hierarchical
taxonomy of classes and their determined roles
cover the basic elements of the university's scientific
activities management. The possibilities of DL
(Description Logics) provide the ability to define
cardinality, hierarchy of roles, inverse and transitive
roles.
OWL axioms of classes and relations were
compiled and attribute constraints were set when
developing ontology O
A
for descriptions for
different characteristics classes and properties, i.e.
(Guarino, 2009):
A. Classes:
RC
U
– university research centers
RC
D
– department research centers
RC
I
– interuniversity research centers
D(x)
–
departments
SM
i
– members of scientific schools
O – organization
SS – scientific schools
IRSS – information resources of research
schools
SD – subdiscipline
ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems
226
Figure 1: A fragment of the scientific activities ontology.
P – persons
PSS – persons at research schools
IR – information resources
B. Roles:
isWorkOrganization – works for the
organization
workSchool – in research school
personHasIR – has publications
publHasDivis – refers to a subdiscipline
C. Axioms of classes and roles:
I
RC
D
RC
U
RC
}.{...}
1
{)(
i
xxxD
OnizationisWorkOrgaP
i
SM .
.SSWorkSchool
SS
i
SS
R.IRpersonHasIP
i
PSS
R.IRpersonHasIIR
i
IRSS
Figure 1 shows a part of scientific activities
ontology as an example.
4 THE PROCESSING OF THE
UNIVERSITY’S INFORMATION
RESOURCES
Figure 2 shows the procedure of the university’s
information resources processing with the purpose to
form the university’s scientific schools and research
areas. It includes the following stages:
Extraction of terminological collocations. C-
value method is used as the method for detecting
collocations (Braslavsky and Sokolov, 2008);
(Min et al., 2012); (Sedova and Kvyatkovskaya,
2011);
Feature selection. test was chosen to assess the
importance of terms (Manning et al., 2009);
Classification of texts according to scientific
areas. The method of k nearest neighbour (kNN)
is used for text classification (Braslavsky and
Sokolov, 2008); (Du and Chen, 2013); (Shengyi
et al., 2012); (Jiang et al., 2012).
A detailed description of processing stages is given
in the following sections.
Figure 2: Stages of the university's scientific resources
processing.
4.1 Automatic Extraction of
Terminological Collocation from
Scientific Texts
Collocation is regarded as a non-random
combination of two or more lexical items common
to most scientific texts. The set of terminological
collocations generated by the specified collection of
scientific texts describes a narrow subject area
(topics and subtopics) of this collection.
For automatic extraction of terminology
collocations from scientific texts that form
ontological vocabulary such methods as mutual
University'sScientificResourcesProcessinginKnowledgeManagementSystems
227
information (MI) method and the C-value method
(Pivovarova and Yagunova, 2010); (Sedova and
Kvyatkovskaya, 2011); (Braslavsky and
Sokolov,
2008) were examined C-value method ranks the
terms based on their frequency and nesting:
a
Tb
a
Tf
a
TP
afa
afa
avalueС
)(
)(
1
)(||log
)(||log
)(
2
2
(1)
where:
)(||log
2
afa
– if a is not into other substrings;
a – is a candidate term;
|| a – is the length of a collocation;
)(af – is the frequency of a collocation;
a
T
– is the set of phrases containing a;
)P(
a
T – is the number of longer collocations
containing
a ;
)f(
a
T – is the sum frequency )P(
a
T .
The main modification of the method based on the
static approach includes the preliminary use of
morphological templates of filters similar to the
following (used abbreviations: n. – noun, adj. –
adjective, g.c. – genitive case, participle - part.)
(Braslavsky and Sokolov, 2008):
[adj. + adj. (genitive case) + n. (genitive case)]
[adj. + adj. + n.]
[adj. + adj. + n. (genitive case)]
[adj. + n. (genitive case) + n. (genitive case)]
[adj. + n.]
[part. + n.]
[n. + n. (genitive case)]
[n. + n.)]
To obtain the list of dominant terms from a
document it is necessary to solve the following
problems:
to get a list of all the terms used in the document;
to select the terms which are dominant in the
given document from the list.;
Phrases similar to the terms are extracted from the
text by C-value method:
the text is divided into strings taking into account
the punctuation, any sequence of words in the
text which are not separated by punctuation
marks is considered;
phrases that meet the following conditions are
extracted from the text:
– morphological analysis of the text (pos
tagging) is performed, each word’s part of
speech and morphological characteristics are
determined. The maximal sequence of words is
a trigram;
– only those collocations which match the
template are selected, stop words which do not
have a semantic load are removed.
entries are created in the database for all
candidate terms which have the value greater
than 1 by C-value method.
Restricting the C-value will only allow considering
the terms longer than one word, because the C-value
of a one-word term is always zero.
Candidate terms obtained in this way form a list
of n-grams (bigram, trigram).
The corpus of documents for processing was
compiled from articles in various fields published in
"Physics of the Solid State" journal founded by the
Russian Academy of Sciences, the Department of
General Physics and Astronomy, Ioffe Physical
Technical Institute, RAS (Science journal "Solid
State Physics, 2013).
Resource: the author’s own development with
the corpus of documents from "Solid State Physics"
journal.
For each candidate term the C-value is calculated
according to formula (1). Table 1 shows the results
of terminological collocations extraction module
work for the "Solid State Physics" area of science
which is a part of created ontology.
4.2 Feature Selection for Classification
Methods of feature selection are used to reduce the
dimension feature space T to form the most
informative space (Manning et al, 2009); (Altınçay
and Erenel, 2010). Feature selection promotes:
learning efficiency by reducing the size of the
vocabulary;
classification accuracy by eliminating noise
characteristics.
Utility measure
),( ctA
of each term in the
vocabulary is calculated for each class,
N terms
having the greatest value
),( ctA
are selected. All
other terms are discarded and they do not participate
in the classification. In this paper three utility
measures are considered: Mutual Information,
criterion
2
X
, Expected Mutual Information.
ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems
228
Table 1: Terminology n-grams (bigrams, trigrams) for
Solid State Physics.
Trigrams
Terms С-value
partial pressure of oxygen 42,79
interstitial silicon atom 38,04
intrinsic interstitial atom 33,28
formation of vacancy micropore 33,28
intrinsic point defect 31,70
strain-hardening coefficient 23,77
amplitude of oscillatory strain 23,77
dislocation-free silicon single crystal 23,77
speed of free surface 22,19
binary solid solution 22,19
dynamic point defect 22,19
transverse size of a crystal 22,19
degree of plastic deformation 19,02
elastic planar mesodefect 19,02
amorphous covalent material 17,43
factor of size mismatch 17,43
Bigrams
plastic deformation 162,50
solid body 139,00
grain size 107,67
point defect 76,31
room temperature 73,00
grain boundary 70,60
oxygen atom 59,56
plastic flow 57,60
Burgers vector 56,09
dislocation density 54,20
Young 's modulus 52,83
deformation rate 49,83
strain hardening 48,56
To remove non-informative terms, i.e. to assess
the importance of terms criterion
2
X
was selected.
Criterion
2
X
is used to test the independence of
two random events, where events are considered
independent. When selecting features the two events
are the appearance of the term and the emergence of
a class.
Experimental evaluation of this method has
shown that it extracts key terms with high accuracy
and completeness (Manning et al., 2009). The
ontological dictionary is formed from these terms,
and it is then used for the classification of thematic
text annotation.
4.3 Text Classification by Scientific
Areas
For the classification of scientific resources kNN-
classification is used. The classification task in
machine learning is a task to assign an object to one
of the predefined classes based on its formal
characteristics.
kNN method (k method of nearest neighbor) is a
vector classification model.
kNN classifier assigns
the document to the prevailing class of nearest
neighbors, where k is the method parameter. The k
parameter in
kNN method is often selected on the
basis of experience or knowledge about the
classification task at hand.
In this paper, the
kNN method is used for
multilabel classification. Classification for classes
that are not mutually exclusive is called multilabel
classification (Malarvizhi and Ramachandra, 2013);
(Ceci et al., 2012).
In multilabel classification
j learning occurs for
different classifiers
j
. Each of the classifiers
returns either class tag
j
c
, or class tag
j
c
, i.e.
},{)(
j
c
j
cd
j
, where d is the tested document.
The document may belong to several classes at
the same time, one class or not to belong to any
class. Classes are conditionally independent of each
other.
The solution of multilabel classification problem
using linear classifiers can be described as follows:
a classifier for each class is created, at that a
training set consists of a set of documents
belonging to the class;
each classifier is applied individually to the
document, the decision of one of the classifiers
does not affect the decision of the other one.
Each document
d in this problem is represented as
a vector
),...,
2
,
1
()(
N
wwwdv
in N - dimensional
space, where each dimension is a description of one
of the document features (Manning et al., 2009);
(Bolshakov et al, 2011); (Liu et al., 2009). The
weight of each feature (term) is calculated as
follows:
d
df
D
dt
tf
dt
w
||
log
,,
(2)
where:
D – is a set of documents of class
j
c ;
|| D – is the total number of documents in the
University'sScientificResourcesProcessinginKnowledgeManagementSystems
229
corpus;
dt
tf
,
– is a term frequency in the document;
d
df
– is the document frequency which is the
number of documents in the collection containing
the
t
term.
To calculate the
k nearest neighbors the cosine
measure was selected as the primary measure of
affinity (Manning et al., 2009); (Du and Chen,
2013); (Shengyi et al., 2012):
n
i
d
i
v
n
i
d
i
v
n
i
d
i
vd
i
v
dvdv
1
2
))((
1
2
))
'
((
1
)()
'
(
))(),
'
(cos(
(3)
where:
)
'
(dv – is the vector space of the document
'
d
of a
training set of documents.
)(dv – is the vector space of the tested document
d
.
In the multilabel kNN classifier class ranks of are
calculated as follows:
k
Sd
dvdvd
c
Idcrank
'
))(),
'
(cos()
'
(),(
(4)
where:
k
S
– is the set of k nearest neighbors of
document
'
d
.
1)
'
( d
c
I
– If (and only if) document
'
d
belongs to
class
c
. Otherwise,
0)
'
( d
c
I .
Thus, the assigned document classes are ranked
according to formula (4).
The classification of scientific resources consists
of several steps:
habituation of the classifier (normalization of
terms of documents’ vector space);
selection of a document;
classification of the document.
Classification of the document consists of the
following steps: a linguistic analysis, extraction of
terms and the document's vector space formation,
calculation of k nearest neighbours of ranking
classes.
As a result of the university’s scientific resources
processing the documents’ profiles are formed. A
document profile is defined as the vector of all
relevant topics of its ontology:
),...,
1
()(
R
d
c
R
d
dPD
(5)
where:
R
d
c
– are relevant topics c of document d .
Accordingly, the academic profile of a staff member
is defined as the profile of all his publications:
),...,
1
()(
R
da
i
R
da
aPD
(6)
where:
R
da
i
– are all the documents of the author.
The final step of the text classification is the
formation of the document’s semantic profile by
creating the individuals of "Information resources"
class in the ontology of scientific research activity.
Figure 1 shows the composition of the university’s
scientific research ontology.
5 EXPERIMENTS AND RESULTS
The semantic portal implemented the possibility of
search for any object of the ontology in the
following classes: researchers, the university’s
research areas, events, key terms, organizations,
departments, sub-departments, the university’s
publications.
Figure 3: A screenshot of the result of the object search in
the knowledge base.
Figure 4: A screenshot of the dynamic page of the
Researcher class instance.
ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems
230
The semantic portal search module supports the
input of inaccurate user requests. Figures 3 and 4
show the search function result at the portal.
6 CONCLUSION AND
PROSPECTS FOR FUTURE
DEVELOPMENTS
The pilot project of the university’s scientific
activity semantic portal has allowed us to generate a
fragment of the university’s scientific knowledge
base and to explore the functionality of the
developed models and methods of text document
processing.
"Solid State Physics" vocabularies have been
formed by the developed methods of automatic
extraction and key terms list selection from the body
of scientific papers.
The topical classification of documents has
allowed the researchers to create the university
researchers’ profiles and to implement a
personalized search engine at the university.
The next stage of the research is the development
of the semantic portal functionality and its
implementation as a part of the university’s
scientific knowledge management system.
ACKNOWLEDGEMENTS
The work was performed under grant "The
development of an e-university's ontological
knowledge base”, state registration number
0213RK00305.
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