SEMANTIC FRAMES

A Way for Automating the Management of Medicinal Documents

Juha Puustjärvi

Helsinki University of Technology, Innopoli 2, Tekniikantie 14, Espoo, Finland

Leena Puustjärvi

The Pharmacy of Kaivopuisto, Neitsytpolku 10, Helsinki, Finland

Keywords: Medical applications, Semantic web, Ontologies, Knowledge management, Semantic interoperability.

Abstract: Knowledge management concerns with acquiring, accessing and maintaining knowledge within an

organization. Knowledge management is also important because organizations view internal knowledge as

an intellectual asset from which they can draw greater productivity, create new value, and increase their

competitiveness. We have investigated the management of knowledge in pharmacies. It has turned out that

the volume of information coming in from a variety of information sources such as pharmaceutical

companies, medicinal wholesalers, social insurance institutions and other authorities is increasing all the

time. Further, the various formats such as paper, fax, email, and a wide variety of multiple electronic media

formats are still complicating the information management. In order to alleviate this problem, we have

introduced the notion of semantic frames, which are included in incoming XML-documents. The frames

specify how to integrate the incoming document into the medicinal ontologies and taxonomies in the

pharmacy system. Further, as our used ontology models the relationships of the incoming documents and

the daily duties, the integration of the documents and daily duties can be automatically done. The gain of

this approach is that the documents (medicinal instructions) are provided just-in-time, and tailored to their

specific needs. An essential prerequisite of our approach is that the healthcare organizations that send the

documents and the receiving pharmacies have to commit to the same medicinal ontologies, i.e., they have to

use the same vocabulary in specifying and interpreting the semantic frames.

1 INTRODUCTION

Healthcare is a field where the fast development of

drug treatment requires specialized skills and

knowledge. As a result the amount of new medicinal

information increases all the time.

The problems arising of increased medicinal

information are discussed in many practitioner

reports and public national plans, e.g., in (Hyppönen

et al., 2005; Dwivedi et al, 2007; Batenburg and

Broek, 2008); Ghani et al., 2008). These plans share

several similar motivations and reasons for the

implementation of medicinal information systems.

These include: reduction of medication errors, better

productivity, and financial savings.

At the same time the technology developed for

managing document has significantly developed.

However this new technology based on Semantic

Web (Daconta et al., Davies et al., 2002) is not yet

deployed in managing medicinal information

systems. This is regrettable since through this

technology many of the goals of the medicinal

information systems could be achieved in an elegant

way.

However, during the past few years several

organizations in the healthcare sector have produced

standards and representation forms using XML. For

example, patient records, blood analysis and

electronic prescriptions are typically represented as

XML-documents (Dolin et al., 2001; Jung, 2005;

Puustjärvi and Puustjärvi, 2006; Bobbie et al., 2005).

This generalization of XML-technologies sets a

promising starting point for the management of

medicinal documents. However, the introduction of

XML itself is not enough but also many other XML-

based technologies (Harold and Scott Means, 2002)

290

Puustjärvi J. and Puustjärvi L. (2009).

SEMANTIC FRAMES - A Way for Automating the Management of Medicinal Documents .

In Proceedings of the First International Conference on Computer Supported Education, pages 290-295

DOI: 10.5220/0001968802900295

 SciTePress

have to be introduced in order to alleviate the

complexity of managing medicinal information (Lin

and Hsieh, 2006; Raisinghani and Young, 2008).

We have investigated the management of

medicinal knowledge in pharmacies. It has turned

out that the volume of information coming in from a

variety of information sources such as

pharmaceutical companies, medicinal wholesalers,

social insurance institutions and other authorities is

increasing all the time. Further, the various formats

such as paper, fax, email, and a wide variety of

multiple electronic media formats are still

complicating the information management.

In order to alleviate this problem we have

investigated semantic exchange of pharmaceutical

information between medical information systems.

By semantic exchange we refer to the ability that the

communicating parties can unambiguously (based

on medicinal ontologies) interpret the exchanged

messages.

In order to alleviate this problem, we have

introduced the notion of semantic frames, which are

included in incoming XML-documents that are sent

by other medical organizations to pharmacies.

Semantic frames are presented in RDF, and they

indicate how the document (medicinal instruction)

relates to the ontologies and taxonomies of the

pharmacy system. Hence, based on the semantic

frame, the integration of the document to the

knowledge base can be automatically done. This,

however, requires that the healthcare organizations

that send the medicinal documents and pharmacies

which receive the documents have to commit to the

same medicinal ontologies, i.e., use the same

vocabulary in specifying and interpreting the

semantic frames.

Further, our used ontology models the

relationships of the incoming documents and the

daily duties in pharmacies, and hence the integration

of the documents and daily duties can be also

automatically done. The gain of this approach is that

medicinal instructions are provided just-in-time, and

tailored to their specific needs.

The rest of the paper is organized as follows. In

Section 2, we give a motivating example of the

problems we have encountered in pharmacies in

managing the incoming information flows. Then, we

consider the way incoming information should be

managed in knowledge centric organizations. In

Section 3, we consider the role of medicinal

ontologies and taxonomies in managing documents.

We first consider the annotation of medicinal

instructions by medicinal taxonomies. Then we

present our used medicinal ontology, which

incorporates documents (medicinal instructions) and

the dispensation of medicinal products as well as

their relationships.

In Section 4, we consider semantic frames from

technical point of view. We first present the SOA-

architecture, where the exchange of semantic frames

takes place, and then we illustrate how semantic

frames are expressed in RDF, and how they are

incorporated in SOAP-messages. Finally, Section 5

concludes the paper by discussing the advantages

and critical issues of our presented approach.

2 MOTIVATING EXAMPLE

A significant problem in pharmacies is that there are

no commonly agreed practices for managing

incoming information. The information management

is more or less haphazard. On the other hand, it is

well known that this is also the case with many other

organizations. However, in pharmacies, the

incoming information is more critical in the sense

that it has direct dependencies on patients’

healthcare.

As an example, consider the activity where a

pharmacist dispenses drugs to the customer and

generates a dispensation note. This operation may

give rise for many kinds of additional

announcements for the customer. For example:

• The pharmaceutical company may have

informed pharmacies that the shape of

the tabs has changed, the size of

package is changed, or the consistency

of the drug has changed.

• Medicinal wholesalers may have

informed of the forthcoming changes on

certain medicinal products.

• Social insurance institutions may have

informed the pharmacy of the changes

concerning the refunds of the dispensed

drug.

• Healthcare authorities may have

informed the pharmacy about the

changes concerning the generic

substitution of the dispensed drugs. In

addition, healthcare authorities may

have set restrictions on physicians’

rights for prescribing certain drugs or

dispensation of certain drugs for certain

patients may be forbidden.

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291

The information of such changes is crucial for

the customer. However, due to the huge amount of

such incoming information there is no hope that the

pharmacist can ensure that she or he has transmitted

all the relevant information to the customer.

Even though all the incoming information were

stored in pharmacy’s information system the

problem still remains, as the relevant information

and its integration into daily duties are not specified

in a machine understandable form. On the other

hand, as the various systems are independently

developed, built based on proprietary solutions,

developed in piecemeal way, and tightly coupled

through ad hoc means, there is no easy way to

develop solutions that require the interoperability of

the various systems.

It is well known that these kinds of problems can

be avoided in knowledge centric organizations

(Davies et al., 2002; Antoniou and Harmelen, 2004).

By the term knowledge centric organization we refer

to the organization which incorporates Semantic

web technologies in information modelling,

presentation, storing and retrieval.

Storing a document in the knowledge base

includes the following steps:

1. Mark up the information with XML using a

relevant XML-Schema.

2. Annotate the information (annotations

specify how the connections to organization

taxonomies and ontologies can be done).

3. Integrate the information to organizations

ontologies.

4. Store the information in an application with

a Web service interface. If this is a new

Web service, it should be registered in the

organization’s registry, along with its

taxonomy classification.

This kind of management of incoming documents

and their associations with organizations taxonomies

and ontologies allows effective search and querying

functionalities.

A problem, however, is that annotating the

documents by appropriate taxonomies and

ontologies (i.e., step 2) has turned out to be hard as

there are no simple way for automating the

annotation. In our approach this problem is avoided

as we assume that the sender (creator) of the

documents has already annotated the document as it

has relevant information to make the annotation.

This however, requires that the communicating

parties commit to the same medicinal ontologies and

taxonomies with respect to the exchanged

documents.

We next consider the medicinal taxonomies and

ontologies on which our solutions are based on.

3 MEDICINAL TAXONOMY AND

ONTOLOGY

3.1 Medicinal Taxonomy

In order to standardize semantic metadata specific

taxonomies are introduced in many disciplines.

In general, taxonomy is a way to classify or

categorize a set of things into a hierarchy (Daconta

et al., 2003). It is a tree like structure consisting of a

root and branches where each branching point (i.e., a

node) and leaf is an information entity. In the

context of information technology taxonomy is

generally understood as the classification of

information entities in the form of a hierarchy, ac-

cording to the presumed relationship of real-world

entities that they represent.

The logic behind taxonomy is that when one

goes up the taxonomy toward the root, the

information entities become more general, and

respectively when one goes down towards the leaves

the information entities become more specialized.

To illustrate this, a simply taxonomy of medicinal

groups is presented in Figure 1. The idea behind this

classification is that the medicinal instructions can

be annotated by the metadata items (the branching

points and the leaves) represented in the tree.

Medical product group

Cardiac drug

Pain drugs

Stomach drugs

Prescription

based pain

drugs

Over the counter

pain drugs

Pain drugs

for topical

use

Aspirin Panadol Ibuprofen

Figure 1: A simple Medical product group taxonomy.

For example, a reason for missing many relevant

documents in keyword based searching is that the

keywords used with queries and documents

metadata descriptions are not standardized by

appropriate taxonomies, e.g., a document may be

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annotated by a keyword “over the counter drug”

while a user uses keyword “self-care drug” in

searching the information.

3.2 Medicinal Ontology

The term ontology originates from philosophy where

it is used as the name of the study of the nature of

existence (Gruber, 1993). In the context of computer

science, the commonly used definition is “An

ontology is an explicit and formal specification of a

conceptualization” (Antoniou and Harmelen, 2004).

So it is a general vocabulary of a certain domain.

Essentially the used ontology must be shared and

consensual terminology as it is used for information

sharing and exchange. On the other hand, ontology

tries to capture the meaning of a particular subject

domain that corresponds to what a human being

knows about that domain. It also tries to characterize

that meaning in terms of concepts and their

relationships.

Ontology is typically represented as classes,

properties attributes and values. So they also provide

a systematic way to standardize the used metadata

items. Metadata items describe certain important

characteristics of their target in a compact form

(Baeza-Yates and Ribeiro-Neto, 1999). The

metadata describing the content of a document (e.g.,

an electronic prescription) is commonly called

semantic metadata. For example, the keywords

attached to many scientific articles represent

semantic metadata

Each ontology describes a domain of discourse.

It consists of a finite set of concepts and the

relationship between the concepts. For example,

within electronic prescription systems patient,

medicinal product, and e-prescription are typical

concepts. These concepts and their relationships are

graphically presented in Figure 2, where ellipses

represent classes and boxes represent properties.

The ontology of Figure 2 includes for example

the following information:

• Taxonomy item is a class, and a parent item

may be associated to a taxonomy item.

Hence, we can model a taxonomy (a

hierarchy of concepts) by the medicinal

ontology.

• Dispensation is a class meaning that each

instance of the class is stored in the

knowledge base. (Semantically a

dispensation is a purchase of a prescription

based medicinal product, i.e., a business

transaction in a pharmacy). Dispensation is

related to an e-prescription, which is related

to a medicinal product. Further an e-

prescription relates to a patient and

physician.

• Instruction is a class, and an instruction

may replace another instruction. Further, an

instruction may be related to medicinal

product, patient or physician.

e-prescription

physician

isPrescribedBy

patient

isTargetedAt

name

Instruction

medicinal_product

includes

physician_association

patient_association product_association

dispensation

relatesTo

name

Tramadol

Norvasc

typeOf typeOf

replaces

taxonomy_item

parent_item

IsAnnotatedBy

Figure 2: A medical ontology.

A significant point in our used medicinal

ontology is that it indicates the possible relationships

of the class dispensation and class instruction.

Hence, in dispensation activity we can query

whether a given dispensation relates to one or more

instructions. And most importantly, such a query can

be automatically generated within each dispensation

activity, and in the case of non empty result, the

relevant instructions are shown to the pharmacist. It

is also possible to visualize the instructions by using

Semantic web technologies as proposed in

(Puustjärvi and Puustjärvi, 2008).

4 REPRESENTING SEMANTIC

FRAMES

Our used architecture is based on Service Oriented

Architecture (SOA) (Singh and Huhns, 2005). It

SEMANTIC FRAMES - A Way for Automating the Management of Medicinal Documents

293

provides flexible methods for connecting pharmacy

systems to the other relevant systems (Figure 3).

SOAP protocol

Social insurance

institutions

system

Pharmaceutical

company’s

system

Medicinal

wholesalers

system

Web service

interface

Web service

interface

Web service

interface

Pharmacy’s

Information

system

Web service

interface

Figure 3: The interaction of medicinal information

systems.

We now illustrate how we can send the

medicinal intructions as well as their semantic

frames by the SOAP protocol.

SOAP was orginally intended to provide

networked computers with remote-procedure call

services written in XML. It has since become a

simple protocol for exchanging XML-messages over

the Web.

A SOAP-message is comprised of a SOAP

header, SOAP envelope and SOAP body which

contains the application-specific message that the

backend application will understand. As illustrated

in Figure 4, we incorporate our used semantic frame

and the instruction in the SOAP body.

HTTP Header

SOAP Envelope

SOAP Header

Headers

SOAP Body

Semantic Frame Instruction

Figure 4: Incorporating a semantic frame in a SOAP

message.

An example of our used SOAP-message is

presented in Figure 5, where the semantic frame is

an RDF-description. The namespaces “mo” and “to”

specify the used ontology and taxonomy,

respectively. The semantic frame indicates that the

instruction “The shape of the tablet has changed” is

an instance (type) of class “instruction”. Further, the

description indicates that the product_association of

the instruction is Tramadol. In addition, the

Instruction is annotated by the taxonomy “medicinal

product group” by the keyword “Prescription based

pain drugs”.

<SOAP-ENV: Envelope

xmlns:SOAP-ENV=“http://schemas.xmlsoap.org/soap/envelope/

”

SOAP-ENV:encodingStyle=”http://schemas.xmlsoap.org/soap/encodig/”>

<SOAP-ENV:Body>

<sematic-frame>

<rdf:RDF

xmlns : rdf=”http://www.w3.org/1999/02/22-rdf-syntax-ns#”

xmlns : xsd=”http://www.w3.org/2001/XMLSchema#”

xmlns : mo=“http://www.lut.fi/ontologies/medical-ontology#”

xmlns : mt=“http://www.lut.fi/ontologies/medical-taxonomy#”>

<rdf:Description rdf:about=”instruction_123”>

<rdf:type rdf:resource=“&mo;instruction”/>

<mo : product _association>Tramadol</mo : recipient>

Prescription_based_pain_drugs

</to : medicinal_product_group>

</rdf : Description>

</rdf:RDF>

</semantic-frame>

<comment> The shape of the tablet has changed </comment>

</instruction>

</SOAP-ENV: Body>

</SOAP-ENV: Envelope>

Figure 5: A SOAP-message including a semantic frame

and an instruction.

5 CONCLUSIONS

Medicinal knowledge is expanding every day. As a

result neither the pharmacists nor other workers in

the health care sector can keep up without the help

of modern information and communication

technology. We have considered this problem in the

case where a pharmacist dispenses drugs to the

customer and generates a dispensation note.

The dispensation may require a variety of

relevant announcements for the customer. However,

due to the huge amount of such incoming

information there is no hope that the pharmacist can

ensure that she or he has transmitted all the relevant

information to the customer.

In order to alleviate this problem we have

introduced the notion of semantic frames, which are

included in incoming XML-documents that are sent

by other medical organizations to pharmacies.

Semantic frames specify how the incoming

document (medicinal instruction) relates to

medicinal ontologies the integration of the document

to the knowledge base can be automatically done.

CSEDU 2009 - International Conference on Computer Supported Education

294

Further, as the used medicinal ontology models the

relationships of the incoming documents and the

daily duties, the integration of the documents and

daily duties can be automatically done. The gain of

this integration is that medicinal instructions can be

provided just-in-time, and tailored to their specific

needs.

An essential prerequisite of our introduced

approach is that the communicating commit to the

same medicinal ontologies, i.e., use the same

vocabulary in specifying and interpreting semantic

frames. This is a critical issue with our approach.

The problem here, as well as with any model that is

based on Semantic web technologies, is that the

creators of the documents (instructions) are

burdened with generating semantic frames.

However, the generation of semantic frames can be

automated by using the tools developed for the

Semantic web.

On the other hand, the introduction of a new

technology is also an investment. The investment on

new ICT-technology includes a variety of costs

including software, hardware and training costs.

Training the staff on semantic web technology is a

big investment, and hence many organizations like

to cut on this cost as much as possible. However, the

incorrect usage and implementation of a new

technology, due to lack of proper training, might

turn out to be more expensive in the long run.

Anyhow, the only way to evaluate definitely our

proposed approach of using semantic frames is to

implement the system in the case of real

applications, i.e., in the environment comprising of

several pharmacies and medicinal organizations.

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