ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A CASE STUDY

International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.1, January 2013

ONTOLOGY-DRIVEN INFORMATION RETRIEVAL
FOR HEALTHCARE INFORMATION SYSTEM :
A CASE STUDY

Aidarus M. Ibrahim1 and Hussein A. Hashi2 Abdullalem A. Mohamed2
1
Faculty of Computer Science and Information system, University Technology
Malaysia (UTM), Malaysia
labiile@hotmail.com
2
oday_hashi@hotmail.com
2
aaldolah@yahoo.com

ABSTRACT
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012

.Keywords

Ontology, Information Retrieval, UTM Clinic, Resource Description Framework(RDF)

1. INTRODUCTION
The field of Information Retrieval (IR) was born [1] to facilitate the fast and relevant retrieval.
Fort the past forty years, the field has full-fledged significantly. Extensive users from different
locations use the IR systems for their daily activity. However, finding relevant and useful
information from large collections of data sources still poses some significant challenges. In this
context, one of the substantial opportunities is to consider the semantics of the information
using ontology. The use of ontology helps to overcome the limitations of keyword-based search
[1] and puts forward as one of the motivations of the present work. While there have been
contributions in this direction during the last few years, most achievements so far either make
partial use of the full expressive power of an ontology-based knowledge, or are based on
Boolean retrieval models, and therefore lack an appropriate ranking model [1] which is needed
for scaling up to massive information sources.

2. RELATED WORK
In the recent past, several ontology-based approaches [1][2] have been proposed. The paper [1]
illustrates reports on the methods, results and experience using a concept-based information
retrieval approach.

DOI : 10.5121/ijnsa.2013.5105 61

Authors [2] evaluated the document adequacy with respect to a query using semantic
proximities between ontology concepts and aggregating models. They described that the IR
system depends on domain ontology to broaden the group of appropriate documents which are
retrieved by employing graphical rending of query results to indulge user communications.
In paper [3], authors presented method for semantic query in out-dated relational database by
creating ontological layer. A schema ontology mined from relational database schema and
modified with global domain ontology is wrapped.

The paper [4] exposes an implementation of ontology and semantic approaches for accessing
data from heterogeneous database. The main idea is to get the appropriate data and pass the
relevant information to web users.

[5]This paper reports the experiment results of the UIUC-IBM team in participating in the
medical case retrieval task of Image CLEF. It is experimented with multiple methods to
leverage medical oncology and user (physician) feedback; both have worked very well,
achieving the best retrieval performance among all the submissions.

In Article [6], described that the improvement of precision-recall of the search results is linked
to semantic based information retrieval technique which needs to understand the meaning of the
concepts in the user query. A huge amount of Relational dataset (RDB) that chains structuring
data in a syntactic base is referred as traditional web. Unfortunately, to convert the available
data stored in relational database into RDF format, which is mostly accepted standard proposed
by the World Wide Web(W3C) is monotonous and error prone.[7,9,8].The authors build a
wrapper that works as translator from semantic queries issued to the system into syntactic data
available within these databases instead of immigrating available legacy data in relational
database into RDF format based ontology. Moreover, another side effect, due to some
limitations in RDF as purely text files [7, 8, and 9]; it can be benefited from the strong
characteristics of relational model to RDF format. Although, the contemporary literature with
regard to semantic search predominantly focuses on ontology query, while a mass of data is
saved in heterogonous relational database. Hence, there is problematic issue which is how to
use sematic query in database [12].

Perez and Conrad [11], showed “Relational OWL”, which routinely fetches the semantic of
relational database and converts into RDF/OWL. This approach enables the usability of
relational database in semantic applications based on the semantic of database structure on the
database contents.

In paper [13] also presented a method to represent the schema and data stored in relational
databases can be represented semantically. This showed how to figure out data items stored in
that specific database. Although, with this approach, the schema and data items of each
relational database are ready for semantic reasoning without the intervention of any expert. The
method did not concentrate the ontological relations between tables and attributes.

The Article [14], The authors stored the RDF data in relational database to achieve the best
possible query results where the actual query is performed. Later, they performed and adequate
mapping of all RDF tipples to the relational data model. This mapping is specific to the needs of
querying normal RDF triples, stored in a special way in relational database. The paper [15],
writers introduced a working draft for an algebraic definition of RDF models. Unluckily, this
work lacks the procedures required for enquiring the RDF graph.

In this paper, we proposed ontology based information retrieval approach resulted from a case
study accomplished in University Technology Malaysia (UTM) Clinic. This paper is organized

62

as follows: Section 2 presents the concepts and ontology based modeling for UTM Clinic.
Section 3 is engaged to show the methodology to build the ontology based information retrieval.
Section 4 demonstrates Query Execution and Information Retrieval Results. Finally, Section 5
concludes the paper.

3. Ontology based Modelling for UTM clinic
Ontology is the specification of concepts and relations. It plays a central role in semantic web
applications by providing a shared knowledge about objects in real world, which promotes
reusability among different modules. Therefore the semantic should be the first concern in any
semantic applications.
For this Case Study, we modeled UTM Clinic ontology.Figure1 shows classes as identified
during the case study. UTM Clinic and person are classes, while staff and patient are sub-class
of person. Moreover, Doctor and Assistant are also sub-classes of Staff. The interesting point
seen from the figure is patient is a sub-class of person. However, Person and Patient can be
shared for some properties such as Fname, Iname, Height and Weight.

Sub-
class Sub-class

Sub-class Sub-class

Figure 1: UTM Clinic Ontology-based Modeling

Figure 2 shows the whole idea for our Case-Study. The functionality and inter-dependency of
classes and objects is figured out here. UTM Clinic has staff. Doctor and Assistant are sub-
classes of staff. Due to the massive task of Doctor, cooperation is needed from the Assistant to
take care of the patient. The four major duties performed by the doctor to see the patient can be
either, medical checkup, consultation, diagnosis or suggestions. Patient can also be person while
person can also a patient. For example, Doctor is a person; on the other hand patient can be a
doctor.

63


Figure 2: UTM Clinic Ontology Representation

4. Retrieval Mechanism
Information retrieval is used to satisfy user’s information needs. In order, to achieve this goal,
Information retrieval deals with representation, storage, organization of, and access to
information items. Therefore, the information retrieval changes the users demand for
information into machine processable query. Usually this query contains a set of terms that
describes the Information needed. As information retrieval mainly deals with natural language,
which might be semantically ambiguous, small errors concerning retrieved objects are
inconsequential. The user may rather be interested in retrieving information about a subject.

In our system as shown in figure3, the user who wants to access the system by retrieving
specific topic using the user interface through which the user can create query that brings the
relevant Information. The user interface submits user query to the wrapper ontology to access
the stored files in the database. The wrapper ontology wraps the relational database with schema
ontology. The result generated by the query is translated using wrapper ontology to bring for the
user. That is why users can issue queries on data semantics without having to use different
ontology language.

64


Figure 3: Retrieval Mechanism

The method implemented below exmins that any semantic retrieval algorithm must be tough
enough to deal with the insuffeicency of KBs and Ontologies as they are not yet sufficiently
developed to depend upon absolutely, and so they propose integrating a keyword based retrieval
method in the overall system. Their semantic ranking method, brought here as an example for
such methods is usually based on two steps:

a. Explaining documents with a weighted annotation, a graphic of the importance of the
explained instance is add to the text. So, each instance is thus weighted,

Figure 4: Annotation Weighing

di - The weight of instance i in document d.

freqd, i - number of occurrences of i in d. maxk{freqd,k} - Maximum number of
occurrences of any instance in d.

N –Total numbers of documents in the search space.

ni - number of documents annotated with i. The idea is that some annotations may bring
us closer to the goal of evaluating the concepts in the document.

Figure 5: document and Query relevance

65


d -The related document is weighted by the vector of annotation

q - A vector resembling the query. qi is the number of variables in the query which are
related to instance i.

b. This approach of ranking is reworking of the classic Vector-space model used for
correspondence evaluation. The final similarity result for the document is:

Where , is the ontology based on similarity index and is the keyword based
similarity indices will be available to soften the error’s implications.

4.1 Method of calculating the weight module

I. The more times the words appear in the document, more possibly it is the
characteristic words;

II. . The measurement of the words will also mark the significance of words.
Seemingly, one idea in the ontology is connected to other notion in that domain
ontology. That also means that the association between two concepts can be
dogged using the length of these two concept’s connecting path in the concept
lattice.

III. If the chances of one word are high, then the word will get additional weight.
One word may be the characteristic word even if it doesn't appear in the
document.

5. Query Execution and Information Retrieval Results
In this experiment, suppose that the user is looking to get single patient’s information .So, the
user builds the query and sends in to the system to get the exact information about the patient.
As we can see from the following figure is the data generated by the system.

The first query process depends on the design of figure1 is extracting a SQL Query. To retrieve
information a SQL statement must go through a process to extract the exact information about
the patient needed by the user. In here, the execution of an RDQL query in RAP consists of four
main steps. At first, the query string is parsed into a memory representation and subsequently
passed to the corresponding engine. In the second step, the query engine searches for triples
matching all patterns from the WHERE clause of the RDQL query and returns a set of variable
bindings. Next, this result set is filtered by evaluating Boolean expressions specified in the
AND clause. The last step is the processing of the final result.

In figure 6, illustrates the basic information related to the patient. We executed different queries
based on out interest and the information that as user we want to retrieve.

66


Figure 6: Information of the patient

In figure 7, we sent a query related to the patient’s name, although same names are
already stored in the database, we can classify using their identity. As shown in the
figure, all the names of the patient that are requested by the user are generated.

Figure 7: Result of Query one

As shown in figure 8, as long as the user creates effective query the better result is
generated. The query below shows, that the user has sophisticatedly based the query on
criteria to obtain the relevant information.

67


The same goes in figure 9; we generated a query related to patient’s information. Handling the
different queries for different purposes makes the system very powerful. All the queries as seen
in the figures are compiled successfully and generated the relevant results associated to the
patient’s information requested by the user.

Figure 8: Result of Query 2

Figure 9: Result of Query 3

5. In conclusion
In conclusion, the paper presents case study performed in UTM Clinic for ontology based
information retrieval approach. Web based approach was developed using PHP, RDF API PHP
(RAP). PHP is a programming language used to develop web based applications. It is powerful
language which can make the web applications portable and easily accessible. RAP is a library
for parsing, querying, manipulating, serializing and serving RDF models. It is developed to
provide API for ontology and RDF schemas the case study focused on producing more relevant
information on UTM clinic system. The main attainment of this case study was to generate a
result from single patient’s information useful for information retrieval.

REFERENCES

[1] Bevan Koopman, Peter Bruza, Laurianne Sitbon2, Michael Lawley”a concept-based information
retrieval approach” Australian e-Health Research Centre, CSIRO” AEHRC & QUT at TREC
2011.
[2] Vishal Jain” Information Retrieval through Multi-Agent System with Data Mining in Cloud
Computing” IJCTA | JAN-FEB 2012.
[3] Mohameth-François Sy, Sylvie Ranwez1Jacky Montmain1, Armelle Regnault, Michel
Crampes1, Vincent Ranwez”User centered and ontology based information retrieval system for
life sciences.” Sy et al. BMC Bioinformatics 2012, 13(Suppl
S4http://www.biomedcentral.com/1471-2105/13/S1/S4.
[4] Mohd Kamir Yusof, Mohd Nordin Abdul Rahman and Mat Atar Mat Amin”Ontology and
Semantic Web Approaches for Heterogeneous Database Access” International Journal of
Database Theory and Application Vol. 4, No. 4, December, 2011.

68

[5] MOSTAFA E. SALEH”Semantic-Based Query in Relational Database Using Ontology”
Canadian Journal on Data, Information and Knowledge Engineering Vol. 2, No. 1, January 2011
[6] Parikshit Sondhi1, Jimeng Sun, ChengXiang Zhai1, Robert Sorrentino,Martin S. Kohn2,
Shahram Ebadollahi, Yanen Li1Medical Case-based Retrieval by Leveraging Medical Ontology
and Physician Feedback: UIUC-IBM at ImageCLEF 2010.
[7] M. Barathi and S.Valli”Ontology Based Query Expansion Using Word Sense Disambiguation.”
(IJCSIS) International Journal of Computer Science and Information Security,Vol. 7, No. 2,
February 2010.
[8] Atsutoshi Imai, and Shuichi Yukita, “RDF Model and Relational Metadata”, 17th
InternationalConference on Advanced Information Networking and Applications (AINA'03).
[9] Ora Lassila, and Ralph R. Swick, “Resource Description Framework (RDF) Model and Syntax
Specification” W3C Recommendation,1999..
[10] Dan Brickley, and R.V. Guha, “Resource Description Framework (RDF) Schema Specification”,
W3CRecommendation, 1999.
[11] Donglin Chen, Liying Li, Rui Wang, An Ontology-based System for Semantic Query over
Heterogeneous Databases, World Congress on Software Engineering, IEEE 09.
[12] Cristian Pérez de Laborda and Stefan Conrad, “Querying Relational Databases with RDQL”
BerlinerXML Tage 2005.
[13] G. Karvounarakis, V. Christophides, D. Plexousakis, and S. Alexaki, “Querying RDF
Descriptions for Community Web Portals”, In BDA’2001, Agadir, Maroc, pages 133–144, 2001.
[14] Sergey Melnik. Algebraic Specification for RDF
Models.http://wwwdiglib.stanford.edu/diglib/ginf/WD/rdfalg/rdfalg.pdf, 1999. Working Draft.

69

ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A CASE STUDY

Recommended

More Related Content

What's hot (16)

Viewers also liked (8)

Similar to ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A CASE STUDY (20)

Recently uploaded (20)

ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A CASE STUDY