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The 10th Conference for Informatics and Information Technology (CIIT 2013)
©2013 Faculty of Computer Science and Engineering
LINKED OPEN DRUG DATA FROM THE HEALTH INSURANCE FUND OF
MACEDONIA
Milos Jovanovik, Bojan Najdenov, Dimitar Trajanov
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University
Skopje, Republic of Macedonia
ABSTRACT
Information dissemination has always been in the focus of
the computer science research community. New ways of
information and data representation, storage, querying and
visualization are being constantly developed and upgraded.
Linked Open Data represents a concept which offers a
comprehensive solution for information and data
dissemination. It accomplishes this by aiming towards two
things: to represent data in an open, machine-readable
format, and to interlink data from heterogeneous
repositories in a way which allows a large variety of usage
scenarios for both humans and machines. On the other hand,
health also represents a domain of high interest in our
research community. In order to provide use-case scenarios
for publishing and using healthcare data in Macedonia, we
generated a dataset of five-star Linked Open Data, based on
the data provided and published by the Health Insurance
Fund (HIF) of the Republic of Macedonia. In this paper, we
describe the process of transforming the data available at
the HIF website, into data published in an open format, and
interlinked with data from the DrugBank domain.
I. INTRODUCTION
The basic idea behind the Open Data concept is that data
which can be considered public should be available in a raw
and machine-readable format, for the purposes of use,
reuse, republishing and redistributing, with little or no
restrictions. When public datasets are published in an open
format, they can be used for building useful applications
which leverage their value and offer different use-cases for
the interested parties [1]. Furthermore, these datasets can
contribute to the overall development of the society, by both
boosting the ICT business sector with new business value and
providing the stakeholders with new functionalities [2].
On the other hand, the concept of Linked Data provides
mechanisms for interlinking data from different repositories
distributed on the Web, in order to provide better data
usage and querying scenarios [3]. Linked Data, in a way,
represents a synonym to the Semantic Web, since its main
goal is interlinking data from the Web by their meaning. The
Linked Data techniques rely on identifying resources with
URIs, providing data about these resources and connecting
them to other resources on the Web, by using standards
such as the Resource Description Framework (RDF) [4].
The Linked Open Data Cloud diagram [5] (Fig. 1) shows the
datasets which have been published in Linked Data format,
along with their interconnections. The figure depicts the
state from September 2011. The datasets are grouped and
shown in different colours based on their domains: media,
geographic data, publications, user-generated content,
government, cross-domain, and health and life sciences.
Figure 1. The LOD Cloud.
Health is a research area which, when it comes to data and
information, is one of the main topics of interest for
computer scientists. Over the years, many different
approaches for representation, storage, querying and
visualizing of health data have been developed. The new
The 10th Conference for Informatics and Information Technology (CIIT 2013)
techniques employed by the Linked Open Data community
offer new ways for covering these areas of interest for health
data. This is one of the main reasons we decided to work
with data from the Health Insurance Fund of the Republic of
Macedonia.
A. Linked Open Data Rating System
In order to encourage the governments, institutions and
people in general to provide and use Linked Open Data, a
star rating system for data has been developed [4].
Figure 2. The Star Rating System.
According to the rating system, every information
published online, regardless of the format, which is made
public with an open licence, can be considered Open Data,
and gets one star. This can be an image, a scan, a PDF file,
etc.
If the data is publicly available on the Web as machine-
readable structured data, then it gets two stars. This can be
an Excel spreadsheet instead of a scanned image. Three stars
are given to data published as structured and machine-
readable, but in a non-proprietary format, such as CSV
instead of Excel.
If the data complies with all of the above rules, and
additionally uses Semantic Web standards (RDF, OWL,
SPARQL) to identify things, so that people can point to it on
the Web, it gets four stars. Five stars are appointed to data
which comply with all of the above rules, and additionally
link to other people’s data, for providing context.
Therefore, when publishing Open Data on the Web, the
most desirable format is the five-star Linked Open Data
format, and it is a goal to which all publishers should aim to.
II. RELATED WORK
Numerous efforts have been made worldwide so far for
transforming healthcare data into Linked Data. The most
notable are the Linking Open Drug Data (LODD) project,
LinkedCT, Open Biological and Biomedical Ontologies (OBO),
and the Semantic Web Health Care and Life Sciences Interest
Group at W3C.
The LODD project1 is focused on interlinking data about
drugs already existing on the Web [6]. The data ranges from
impact of the drugs on gene expression, through to results of
clinical trials. The aim of the project is to enable answering of
interesting scientific and business questions by interlinking
previously separated data about drugs and healthcare. As
part of their work, they have collected datasets with over 8
million RDF triples, interlinked with more than 370.000 RDF
links (Fig. 3).
Figure 3. Part of the LODD Cloud.
One of the datasets, which is a part of the LODD cloud, is
DrugBank2. It provides RDF data about drugs, such as
chemical, pharmacological and pharmaceutical information,
taken from an existing base3 of drug data on the web. The
DrugBank RDF dataset contains over 766.000 RDF triples for
4.800 drugs. Because of its size, we decided to use this
dataset as a reference point for the drug data from the
Health Insurance Fund which we describe, publish and
interlink.
1 http://www.w3.org/wiki/HCLSIG/LODD
2 http://wifo5-03.informatik.uni-mannheim.de/drugbank/
3 http://drugbank.ca
The 10th Conference for Informatics and Information Technology (CIIT 2013)
LinkedCT4 is a project aimed at publishing clinical trials
data in a Linked Data format [7]. They transform existing
clinical trials data into RDF, discover semantic links between
the records themselves, and link to other data sources such
as PubMed5, as well. The datasets from LinkedCT are also
part of the LODD project (Fig. 3).
OBO Foundry6 – the Open Biological and Biomedical
Ontologies project, is a collaborative effort involving biology
researchers and ontology developers who work together to
develop a set of design principles for ontology development
in the biomedical domain. As a result of the project, eight
ontologies have been developed, and a large amount of
others hold the status of candidate ontologies. The domains
of the ontologies are mainly bio processes, anatomy,
biochemistry, proteins, etc.
The Semantic Web Health Care and Life Sciences7 is an
interest group at the World Wide Web Consortium (W3C),
comprised of experts from around 30 W3C member
organizations: research centers, universities, companies,
health institutions, etc. Its mission is to develop and support
the use of the technologies of the Semantic Web in the fields
of healthcare, life sciences, clinical research and translational
medicine [8]. It is comprised of various subgroups, which are
focused on making the biomedical data available in RDF,
developing and maintaining biomedical ontologies, etc.
In Macedonia, the Open Data and the Open Government
Data initiatives started around 2011. The Macedonian
Government officially joined the global Open Government
Partnership (OGP) initiative8 in April 2012, and took part in
the annual OGP meeting in Brazil, the same month. After a
few months of gathering data from the ministries and the
government institutions, the Macedonian Government
published the official Macedonian Open Government Data
portal9. The portal currently holds open data from twelve
ministries, six government institutions and three
independent institutions. The data on this portal are mostly
published as one-, two-, or three-star data, or represent links
to already existing and specialized applications which the
4 http://linkedct.org/
5 http://www.ncbi.nlm.nih.gov/pubmed
6 http://obofoundry.org/
7 http://www.w3.org/blog/hcls/
8 http://www.opengovpartnership.org/
9 http://opendata.gov.mk/
different institutions have developed for their needs in the
previous years. From the healthcare sector, the portal holds
data from the Ministry of Health, which is mainly data about
public health institutions and their licenses, published as
three-star data, in CSV format.
Besides the official Government activities, there have been
other Open Data and Linked Data activities in Macedonia,
mainly from the academia, which include the development
of a Crime Map for the Republic of Macedonia [9], based on
the public bulletins from the Ministry of Internal Affairs, as
well as the opening and linking of data from the Universities
in Macedonia [10]. Our Faculty had an industry project from
which two Open Data mobile and web applications were
developed based on the data from the Health Insurance
Fund of Macedonia. The data we used for the project were
three-star data, in CSV and XML format, acquired by
transforming the publicly available data from the Fund which
they publish on their website.
Apart from these, there have not been other Open Data
and Linked Data activities involving healthcare data from
Macedonia.
III. LINKED OPEN DATA FROM THE HEALTH INSURANCE
FUND
The Health Insurance Fund of the Republic of Macedonia is
an institution which is responsible for regulating and
managing the public services for primary healthcare,
specialist healthcare, and hospital healthcare. Additionally,
the Fund along with other government institutions regulates
the list of drugs which are covered by the health insurance,
and defines the referent (nominal) prices for certain drugs.
With this position in the society, we believe that the data
which the Fund works with is of high importance, and there
would be a great benefit of opening their public data in RDF,
and interlinking it with other datasets from the LOD and
LODD clouds.
A. Public Data from the Fund
The Health Insurance Fund of the Republic of Macedonia
has been publishing their public data on a regular basis on
their website10. These data contain information about
healthcare services and their prices, statistics about the rate
10 http://www.fzo.org.mk/
The 10th Conference for Informatics and Information Technology (CIIT 2013)
of usage of hospital beds, reports from the inspections in the
public and private healthcare institutions, financial data
about the Fund, insurance information, referent drug prices,
private and public healthcare institutions which the Fund
works with, etc. The Fund has not yet published its data on
the official Macedonian Open Government Data portal.
Although the data from the Fund’s website can be
technically considered as Open Data, they are mainly
published in PDF and Excel formats, making them only one-
star and two-star data. In order to leverage the usability of
the public data from the Fund, we decided to transform
them into five-star Linked Open Data: to first transform them
into RDF, and then interlink them with data from other
publicly available datasets from the LOD and LODD clouds.
As a starting point, we chose the drug datasets from the
Fund, which contain pharmacological and pharmaceutical
information, along with the referent price for different drugs.
B. Ontology
The Fund has published their public drug data in various
datasets, which contain different sets of information. These
datasets hold information about the brand name, the generic
name, the manufacturer, the referent price, the packaging,
the strength, and the dosage form for drugs. Additionally,
each drug is identified by an ID generated by the Fund, as
well as a globally identifiable ATC code, used for
classification of drugs and controlled by the World Health
Organization.
In order to transform and represent the drug data in RDF,
we needed an ontology. Following the best practices for
ontology development, we decided to re-use already existing
drug ontologies. In the process of choosing an ontology for
re-use, we had to bear in mind the interlinking part of the
process, which meant that we need an ontology used by a
drug dataset which we would connect our data to, later in
the process. With this in consideration, we decided to use
the DrugBank RDF repository and its ontology.
Table 1. The properties from the DrugBank ontology which
we use.
DrugBank property
Description
atcCode
The global ATC code of the drug.
genericName
The generic name of the drug.
brandName
The brand name of the drug.
The DrugBank ontology contains the class ‘drugs’, which
represents the drug entities. It also contains relations for the
ATC code, the generic name and the brand name. We used
the ‘drugs’ class along with the ‘atcCode’, ‘genericName’ and
‘brandName’ properties (Table 1, Fig. 4).
Figure 4. The HIFM Ontology.
Table 2. The properties in the HIFM ontology.
HIFM property
Description
id
The ID of the drug, as defined by the
Health Insurance Fund.
manufacturer
The name of the manufacturer of the
drug.
refPriceNoVAT
The referent (nominal) price in
Macedonian denars (MKD), without the
VAT tax.
refPriceWithVAT
The referent (nominal) price in
Macedonian denars (MKD), with the VAT
tax.
packaging
The type of packaging of the drug.
dosageForm
The dosage form of the drug.
strength
The strength of the active substance in
the drug.
similarTo
This property points to other drugs which
have the same active substance and
indications, but may come in different
strengths and from different
manufacturers.
The 10th Conference for Informatics and Information Technology (CIIT 2013)
However, we still needed properties for describing the
other drug information, not covered by the DrugBank
ontology. Therefore, we developed our own ontology: the
HIFM ontology (Fig. 4). The HIFM ontology contains its own
class for drug type entities, ‘Drug’, seven datatype properties
and ‘similarTo’ as an object property (Table 2, Fig. 4).
Along with the properties taken from DrugBank, and the
properties defined in our HIFM ontology, we use the
‘rdfs:label‘ and ‘owl:seeAlso’ properties. The ‘rdfs:label’
property is used to point to the generic name of the drug,
whereas ‘owl:seeAlso’ is used to link the drugs from our
HIFM graph with drugs from DrugBank. This will be
elaborated in more details further in the paper.
C. Mapping the Data from CSV to RDF
The next step was to map and transform the public data
from Excel to RDF. For this, we decided to use the Virtuoso
Universal Server11, which provides mechanisms for data
transformation and management, for various types of data,
including the Semantic Web standard representation format,
RDF. It serves as a Linked Data server, as well, and allows
local and remote data querying with the Semantic Web
query language, SPARQL.
The mapping process consisted of two steps. First, we
imported the CSV files (generated from the Excel files
available on the website of the Health Insurance Fund) into
relational databases in Virtuoso. Then, with the use of
R2RML12, the mapping language for transforming RDB data
into RDF data, which is also a part of Virtuoso, we created
RDF Views over the relational databases. These RDF Views
allow data management with the use of the technologies of
the Semantic Web, such as querying with SPARQL, over data
which resides in standard relational databases.
The R2RML mapping was done with the use of mapping
files, which contain information about the transformation of
the RDB tables, columns and cell values into RDF triples, with
a subject, a predicate, and an object. In this step we used our
HIFM ontology, as well as parts of the DrugBank ontology
which were previously discussed. As an identifier of the
drugs we chose the ID value, assigned to the drugs by the
Fund. Each of the drugs was set to be both of
‘drugbank:drugs’ and of ‘hifm:drug’ RDF type, and the values
11 http://virtuoso.openlinksw.com/
12 http://www.w3.org/TR/r2rml/
for the ATC code, the generic and brand name, the dosage
form, the strength, etc., were described using the DrugBank
and HIFM properties (Fig. 4, Table 1, Table 2).
Since the different Excel files from HIF’s website contained
different subsets of drug data, the process resulted in several
different graphs, with different sets of information about the
drugs. In order to create one single graph with all of the
information, we used the SPARQL endpoint from Virtuoso
and with the use of the SPARQL query language, we
matched, combined and inserted the data from the other
graphs into one single graph. The matching of the drugs was
done by their ID, assigned by the Fund, which was present in
all of the Excel files.
D. Transforming the RDF Data into Linked Open Data
Once we had all of the drug data into an RDF graph in
Virtuoso, we proceeded with interlinking the drugs among
themselves and with other drugs available in the LOD and
LODD clouds.
For the purpose of interlinking the drugs from the Fund
between themselves, we created a property in the HIFM
ontology, called ‘similarTo’ (Fig. 4, Table 2). This property has
the purpose to link Drug A to Drug B (and vice-versa), if their
first seven characters from the ATC code match. Even though
the ATC codes should have seven digits, the ATC codes which
the Fund assigns to the drugs in Macedonia contain ten
digits. These additional three digits are used for marking a
difference between drugs which have the same active
substance, but come in different strengths, packages and can
be from different manufacturers. So, in order to support a
use-case scenario in which a user would be interested in
drugs similar to the one he or she is looking for, we decided
to create a ‘similarTo’ relation between each two drugs from
our dataset which have the same first seven digits in the ATC
code. The relation is defined as both transitive and
symmetric in the ontology, which allows more flexibility in
the process of querying the data.
In order to transform our drug data into five-star Linked
Open Data, we needed relations in the RDF graph towards
outside entities. For this purpose, we decided to use the
DrugBank dataset, which is the largest and the most detailed
drug dataset on the Web. Similarly as in the process of
interlinking the drugs internally, we used the ATC codes to
detect the similarity between the drugs from our dataset and
the drugs from DrugBank. For this purpose, we matched the
The 10th Conference for Informatics and Information Technology (CIIT 2013)
first seven digits from the ten-digit ATC code in our dataset,
with the seven-digit ATC code in the DrugBank dataset. Once
the drugs were matched, we added new triples within our
graph, denoting that the drug defined in our dataset had an
‘owl:seeAlso’ relation to the drug defined in the DrugBank
dataset. This relation provides new possibilities for data
querying, since we can now move from our local drug
dataset and get information which is not present locally, but
somewhere on the Web, in the LOD and LODD clouds.
An example RDF representation of a particular drug in our
graph, denoted with all of its properties and relations to
other drugs, both from the same graph and from DrugBank,
is shown in Fig. 5.
Figure 5. An example Drug from the HIFM Graph.
We choose the ‘owl:seeAlso’ relation from the commonly
used OWL namespace, over the ‘owl:sameAs’ relation,
because we cannot guarantee that the two drug descriptions
refer to the same real-world entity. For instance, a drug in
our dataset contains information about a manufacturer,
dosage form, strength and price, i.e. a drug as a product. On
the other hand, a drug in the DrugBank dataset contains
information about the chemical formula, molecular weight,
affected organisms, interactions, etc, i.e. information about
drugs as active substances, which have the same effect and
indications, but can be marketed and sold in various
packages, forms, strength, by different manufacturers.
E. Publishing the Linked Open Data
Once we had a graph of Linked Open Data from the Health
Insurance Fund of Macedonia, the next step was to publish
the data on the Web. For this purpose, we created a public
instance13 of Virtuoso at the Faculty of Computer Science
and Engineering, in Skopje. This Virtuoso instance holds the
Linked Drug Data from the HIFM graph, and provides a public
interface via its SPARQL endpoint14. The endpoint can be
used for querying the drug data from the graph, either by
using the SPARQL editor available at the endpoint, or by
using the endpoint as a web service from a mobile, web or
desktop application. The endpoint can be used as a web
service by adding the SPARQL query into a query string,
appended to the URL of the endpoint.
Additionally, we made dumps of the HIFM graph data into
RDF files, represented in RDF/XML, Turtle, N3, RDF/JSON and
JSON-LD semantic data formats. These RDF dumps are
published on a public CKAN instance15 at the Faculty of
Computer Science and Engineering, in Skopje. This instance
represents a CKAN catalogue of Open Data maintained and
published by the Faculty. The users can freely access and
download the data from the catalogue, and use it in their
own applications.
IV. USE-CASES
The purpose of using Linked Open Data is the ability of
leveraging the value and usability of the data, in various use-
cases. Once we have the local HIFM drug data interlinked
with data from the LODD cloud, we can start querying the
local data and continue moving through the links to
information published elsewhere on the Web. This ability
broadens the usage possibilities of the data, and allows
development of new types of applications over the data.
A. Using Information from HIFM
Once such use-case would be to use the ‘hifm:similarTo’
relation to retrieve information about drugs which have the
same active substance as the drug we are interested in, but
may have a different brand name, different price, may be
manufactured by a different company, and may have a
different package form and strength.
13 http://linkeddata.finki.ukim.mk/
14 http://linkeddata.finki.ukim.mk/sparql
15 http://data.finki.ukim.mk/
The 10th Conference for Informatics and Information Technology (CIIT 2013)
For instance, if we are looking at information about the
drug “NIFADIL, film coated tablets, 50 x 10mg” from the
HIFM graph, and we want to find out the drugs which are
similar to it, we can use the following SPARQL query:
PREFIX drugbank: <http://wifo5-04.informatik.uni-
mannheim.de/drugbank/resource/drugbank/>
PREFIX hifm: <http://www.fzo.org.mk/ontology/hifm#>
SELECT ?bn ?p ?m
WHERE
{
hifm:79588 hifm:similarTo ?dbd .
?dbd drugbank:brandName ?bn ;
hifm:refPriceWithVAT ?p ;
hifm:manufacturer ?m .
}
ORDER BY ASC (?bn)
The query first makes a lookup for RDF triples in the HIFM
graph where the subject is the drug we are currently
interested in, and it is in a ‘hifm:similarTo’ relation with
another drug from the HIFM graph. The drugs similar to the
drug with ID = 79588 are placed in the ?dbd variable. Then,
we look up the details for these drugs, and select their brand
name, the price and the manufacturer (Table 3).
Table 3. Results from the SPARQL query.
Brand Name
Price
Manufacturer
CORDIPIN R, 30 x 20mg
14,00
KRKA
CORDIPIN XL, 20 x 40mg
19,00
KRKA
KORINCARE NEO, 20 x 40mg
19,00
TCHAIKAPHARMA
KORINCARE, 20 x 20mg
9,00
TCHAIKAPHARMA
NIFADIL RETARD, 30 x 20mg
14,00
ALKALOID
NIFEDIPIN RETARD, 30 x 20mg
14,00
REPLEKFARM
NIFEDIPIN, 50 x 10mg
35,00
JAKA 80
NIFELAT RETARD, 30 x 20mg
14,00
ZDRAVLJE
This query can be written and executed directly in the
SPARQL editor at our Virtuoso SPARQL endpoint, or can be
sent as a query string from an application, and used as a web
service. The web service calls have the following format:
http://linkeddata.finki.ukim.mk/sparql?default-graph-
uri=DEFAULTGRAPH&query=SPARQLQUERY&format=FORMAT
Here, DEFAULTGRAPH represents the graph URI of the
default graph for the query, i.e. the graph the query should
be executed over, SPARQLQUERY represents the SPARQL
query, as the one shown above, and FORMAT represents the
format of the response. The different format supported by
the Virtuoso SPARQL endpoint include HTML, XML, JSON,
Javascript, CSV, Spreadsheet, RDF/XML, N3, Turtle, etc.
With this, a developer of an mobile application over the
Linked Open Data from the Fund could easily develop a
functionality which, based on the current drug the user is
browsing, could offer him alternative drugs which may be
more accessible, easier to find, and even cheaper. This would
provide the end-user of the application with a better insight
into his options as a patient when buying drugs.
B. Using Information from DrugBank, LOD and LODD
Now that the HIFM graph contains links to another dataset
on the Web, we can use them to traverse the remote graph.
This way, by using the ‘owl:seeAlso’ relation, we can retrieve
information from the DrugBank dataset which are not
present in the local HIFM drug data.
For instance, if we want to get information about the food
interactions of the drug “DILACOR, tablets, 20 x 0,25mg”, we
can use the following SPARQL query:
PREFIX hifm: <http://www.fzo.org.mk/ontology/hifm#>
PREFIX drugbank: <http://wifo5-04.informatik.uni-
mannheim.de/drugbank/resource/drugbank/>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
SELECT ?fi
WHERE
{
hifm:32964 owl:seeAlso ?dbd
SERVICE <http://wifo5-04.informatik.uni-
mannheim.de/drugbank/sparql>
{
?dbd drugbank:foodInteraction ?fi .
}
}
ORDER BY ASC (?fi)
This SPARQL query starts from the HIFM graph, looking for
all of the triples which state that the drug with ID = 32964 is
in a ‘owl:seeAlso’ relation with another drug. The drugs from
the matched triples are selected as an ?dbd SPARQL variable,
which is then used in the next line and is sent to the SPARQL
endpoint at DrugBank. This line asks for triples which will tell
us the food interaction for the drug(s) represented by the
?dbd variable. The resulting food interactions will be
returned in the ?fi variable, which is then displayed as a
result (Table 4).
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Table 4. Results from the SPARQL query.
Food Interactions
Avoid avocado.
Avoid bran and high fiber foods within 2 hours of taking this
medication.
Avoid excess salt/sodium unless otherwise instructed by your
physician.
Avoid milk, calcium containing dairy products, iron, antacids, or
aluminium salts 2 hours before or 6 hours after using antacids
while on this medication.
Avoid salt substitutes containing potassium.
Limit garlic, ginger, gingko, and horse chestnut.
This information was not stored in our local HIFM dataset,
but because of the Linked Open Data principles and the links
we provided to drugs published at DrugBank, we were able
to retrieve additional information for the given drug. We can
use these types of queries for retrieving any other
information which DrugBank provides, for our drugs, defined
in our HIFM graph.
Note that if the drug with ID = 32964 from the local HIFM
graph has more than one ‘owl:seeAlso’ relations with drugs
defined in the DrugBank dataset, the result of the query
would be a union of all of the food interactions from the
different drugs it is similar to. This can happen when a drug
from the HIFM graph has more than one active substance,
and they are defined as different drugs in the DrugBank
dataset.
This use-case can provide a developer of a medical mobile,
web-based or desktop application, with a functionality which
would give its end-users additional and vital information
about the drugs they are browsing.
The DrugBank dataset contains links to other datasets as
well (Fig. 1, Fig. 3), and we can use them for accessing other
LOD and LODD cloud datasets. For instance, the DrugBank
data contain ‘owl:sameAs’ relations to drugs which are
described as part of clinical trials in the LinkedCT dataset. In
the same manner as we leap from our HIFM graph to the
DrugBank graph, we can continue over to the LinkedCT
graph, and gather the needed information from there.
V. CONCLUSION AND FUTURE WORK
The Open Data concept has gained momentum in the last
years. The governments and government institutions from
the world leading countries are proactively providing their
public data in an open, machine-readable format free for use
and re-use by the citizens, developers and business entities.
In order for the utility of data in open format to be
maximized, they need to be transformed into five-star Linked
Open Data. This means that they first need to be described,
published on the Web, and used, with the W3C Semantic
Web standards, such as RDF, OWL and SPARQL. Next, they
need to have links towards data published on other locations
on the Web, in order to provide context and broaden the
data description. This way, once we start going through data
from one RDF graph, we can easily traverse into another
graph and use and retrieve its information. This is not limited
to only one step; regardless of the starting point, we can
traverse any number of interlinked RDF graphs, regardless of
their location. This is the main idea behind the LOD and
LODD clouds of datasets.
In this paper, we gave an overview of the process of
transforming the two-star data which the Health Insurance
Fund of Macedonia publishes on their website, into five-star
Linked Open Data, connected to the DrugBank dataset, and
from there, indirectly with the entire LOD and LODD clouds.
The result of the transformation was a HIFM graph, which
contains over 21.000 RDF triples for 1.020 drugs from the
Fund. These drugs are interconnected with 9.946
‘hifm:similarTo’ relations with each other, and with 1.015
‘owl:seeAlso’ relations to drugs from the DrugBank dataset.
The HIFM graph is available for use at the Virtuoso instance
at our Faculty, and as an RDF dump on the CKAN instance at
our Faculty.
We also provided use-cases which give examples of how
the data from the Health Insurance Fund and DrugBank can
be used, in order to provide application developers with
mechanisms and ideas for retrieving distributed data in
various formats.
Future work on the project would include other datasets
from the Health Insurance Fund, as well as interlinking the
already existing HIFM graph with data from other LOD and
LODD member datasets. The Fund also has data about the
private and public healthcare institutions, and the services
they offer. Potentially interesting use-cases arise from
providing five-star Linked Open Data about these
institutions, such as the services they provide, the price of
the services, the location of the healthcare institutions,
contact information, etc. This data could then be used by a
The 10th Conference for Informatics and Information Technology (CIIT 2013)
mobile application which would suggest the nearest
pharmacy, or dentist, or laboratory to a user, based on his
needs, preferences and location. The Linked Open Data
characteristic of the data would allow one such application
access to other valuable information available elsewhere in
the LOD and LODD clouds, thus leveraging the usability of
the data and the application at the same time.
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