Knowledge Security

An Empirical Use of IT – Child Abuse Monitor System Model

Tiago Pereira

and Henrique Santos

Algoritmi Centre, University of Minho, Azurém, Guimarães, Portugal

Information Systems Department, University of Minho, Azurém, Guimarães, Portugal

Keywords: Health Care Knowledge Sensitivity, Health Care Decision Support System, Ontology, Health Care

Knowledge Security, Knowledge Management, Topic Models, Information Retrieval, Text Mining.

Abstract: The Information Security, nowadays, faces new threats such as the process of massive information in which

are applied artificial intelligence techniques with the goal of predicting and classify our actions. Thus,

knowledge about our behaviour, likes, dislikes, among others, leads us to consider that Knowledge Security

appears has the natural evolution of Information Security. On the other side of the same coin we have new

possibilities to monitor health, the wellbeing and abnormal symptoms, reactions to treatments, alert for

insulin insufficiency, pacemaker malfunction, among others. Child abuse cases, it is a subject of most

importance in our society, although, these cases are, from suspicion to signalization, difficult to identify

since strong evidences are needed. Typically, health care services deal with these cases in an earlier stage

with evidences based on the emergency diagnosis, but, yet, not sufficient and with lack of information, thus,

further analysis is needed from experts’ teams. The main goal of these teams is to protect the child from the

possibility of occurrence of more abuses. We have developed a prototype that automatically could predict

and alert to situations that could be needed to use the measure of the protection of the child, using

digitalised child abuse processes, knowledge management and artificial intelligence techniques with 83% of

true positives. In this research, we addressed both sides of the coin, Knowledge Security and the benefits of

the Knowledge Discovery defining, in our opinion, the fourth generation of Knowledge Management -

Value Creation and Knowledge.

1 INTRODUCTION

Knowledge Security it is seen here as a natural

evolution from information security. Currently, we

face new paradigms concerning the context of

“BigData", the huge amount of data that is

processed, the machine learning algorithms and

other artificial intelligence which are implemented,

in such way that is possible to predict our present

and future actions, in other words, knowledge about

our behaviour, preferences, among others. Thus, the

protection of the right to privacy, promotion of

confidentiality, integrity and availability -

fundamental properties of InfoSec (ISO, 2013,

Oladimeji et al., 2011). The context of knowledge

Security demands a new approach. To promote,

protect and preserve knowledge about us is

necessary to concern about knowledge management

and information security management that indicates

that we should define what we need to protect. This

decision, it is related to the value of that knowledge

represents to us. Concerning the organizations, the

situation is similar. If we want to implement security

treatments, we should define the security object to

protect. In this research, the health care issues were a

mean to demonstrate the need of knowledge

security, also to realize that in what kind of form

Knowledge Sensibility allow the characterization of

critical knowledge. Based on this premise, we have

formulated the research question: Can we

automatically classify health care information as

critical concerning laws and regulations, terms and

knowledge sensibility in order to preserve it? Using

the design science research methodology, we have

developed a prototype that can distinguish child

abuse documents from others based on health care

knowledge (symptoms, effects, among others)

allowing us to identify a case that should have

further analysis by child protection committee and

by its critical value if it should or not be

236

Pereira, T. and Santos, H.

Knowledge Security - An Empirical Use of IT – Child Abuse Monitor System Model.

DOI: 10.5220/0006384002360243

In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2017), pages 236-243

ISBN: 978-989-758-251-6

implemented the child protection by prevention. The

Childhood protection is a subject with high value for

the society, but, the Child Abuse cases are difficult

to identify (JN, 2013). The process from suspicious

to accusation is very difficult to achieve. It must

configure very strong evidences. Typically, Health

Care services deal with these cases from the

beginning where there are evidences based on the

diagnosis, but they aren't enough to promote the

accusation. Besides that, this subject is highly

sensitive because there are legal aspects to deal with

such as: the patient privacy, paternity issues, medical

confidentiality, among others.

2 HEALTHCARE CRITICAL

KNOWLEGE

The development of standards of health care

software is a big step to the interoperability between

information systems in this area. There are, at least,

six entities that have developed standards in this

field: The American Society for Testing and

Materials with ASTM-E31, The American National

Standards Institute with ANSI-HL7, The European

Committee for Standardization with CEN-TC251,

The International Organization for Standardization

with ISO-TC215, The Association of Electrical

Equipment and Medical Imaging Manufactures with

NEMA-DICOM and IEEE with multiple standards

(IRMA, 2013). With the evolution of the health care

information systems the access to patient

information using Electronic Health Record Systems

(EHRS) is facilitated, e. g. Urgency treatment data,

health monitoring data, among others. According to

the health domain analysis report from the technical

committee from HL7 about security and privacy of

health care information, particularly, in exchange of

information between information systems and

according to HL7 Security and Privacy Ontology

was possible for us to identify critical knowledge

concepts in health care domain (WG, 2013, HL7,

2010): Substances abuse, Sexual abuse and domestic

violence, Genetic disease, Sexual transmitted

disease, Sickle Cell, Sexuality and Reproductive,

HIV/AIDS, Psychiatry and Taboo (HL7, 2010), see

figure 1.

From this, we explore the subject General

Abuses with the focus on the child abuse and based

on regulations and legal documentation we have

constructed an ontology that maps the concepts:

symptoms, behaviour and other evidences of child

abuse (Saúde, 2008). For the different phases and

objectives of this research we have used multiple

research techniques: survey literature techniques,

content analysis and proof of concept in Design

Research context.

“Design Science research is a research paradigm

in which a designer answers questions relevant to

human problems via the creation of innovative

artefacts, thereby contributing new knowledge to the

body of scientific evidence. The designed artefacts

are both useful and fundamental in understanding

that problem” (Hevner and Chatterjee, 2010).

The design science research has its roots in the

sciences of the artificial. Artificial as something that

is created by humans that doesn’t exists in Nature.

Design Research is fundamentally a problem-solving

paradigm. It consists in seeking innovation through

ideas, practices, technical abilities and products

obtained from a set of routines such as: analysis,

design, implementation, and use of information

systems concerning the effectiveness and efficiency

achievement on organizations. The outputs forms of

Design research could be: constructs (vocabulary

and symbols), models (abstractions and

representations), methods (algorithms and practices),

and instantiations (implemented and prototype

systems).

3 CHILD ABUSE CRITICAL

KNOWLEDGE MONITORING

SYSTEM

The model of the system, see figure 1 and 2, is

defined by four components: the knowledge capture

component that allows to extract topics and the

descriptors from documents, the critical knowledge

ontology component that is used to filter the critical

topics and descriptors addressing the context of

ontology; the critical knowledge repository

component that stores all files produced in all

process of the system; and the alert and log

component that we need the use of Artificial

Intelligent algorithms and techniques that allow to

predict actions. Each component is based on a

variety of information systems fields (Pereira and

Santos, 2012, Pereira and Santos, 2013a).

Knowledge Security - An Empirical Use of IT – Child Abuse Monitor System Model

237

Figure 1: Child Abuse Critical Knowledge Monitoring System.

3.1 The Knowledge Capture

Component (KC)

The knowledge capture component requisites are:

extracting tokens from documents in a variety of

formats, such as text and audio. Additionally, the

implemented system supports other formats: video

(extracting sound and text within the video),

webpages, among others; and transform the tokens

extracted in such format that could be searchable

concerning the privacy and confidentiality, integrity

and availability of documents. In order to do it, we

have implemented a topic model approach using two

methods, Latent Dirichelet Allocation - LDA

(Hofmann, 2001, Steyvers and Griffiths, 2007) and

Pachinko Allocation Model - PAM (Mimno et al.,

2007, Pereira and Santos, 2015) and not that kind of

techniques that, normally, extract names, locations,

among others. The PAM has been chosen because it

can establish relations between topics and topic

descriptors. The use of the topic model approach is

fundamental because allows to driven topics from

documents and ignore (because of its lower

occurrence within a document) personal data

(names, contacts and addresses) complying with the

privacy and information security properties. To use

topic models from the extracted tokens from the

documents we needed to filter (Efraim Turban,

2010) them, essentially, tokens less than four

(configurable) characters and trivial discourse tokens

such as “and”, “or”, punctuation, among others. As

an output of this component we get a searchable set

of descriptors clustered by topics that co-occur in the

document.

Table 1: Knowledge Science fields used in Knowledge

Capture Component.

KC Component – Knowledge fields

TextMining

Information Retrieval

3.2 The Critical Knowledge Ontology

Component (CKO)

The critical knowledge ontology component

requisites are: allowing the editing of the critical

ontology; and matching the ontology with the output

of the knowledge capture component. This

component uses Portégé Editor and Portégé API, see

acknowledgments, for the matching procedure with

the topic descriptors.

Table 2: Knowledge Science Fields used in Critical

Knowledge Ontology Component.

CKO Component – Knowledge fields

Health Care / Child Abuse - Sensitivity

Knowledge Engineering

Knowledge Management

3.3 The Critical Knowledge Repository

Component

The critical knowledge repository component

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238

Figure 2: Critical Knowledge Monitor System Model.

requisites are allowing the storage of the outputs

from the KCC and CKOC. This component uses a

document management tool with the control version

capability. The control versions could be useful in

future implementations and we could analyse

multiple diagnosis of the same patient in an

historical perspective.

Table 3: Knowledge Science fields used in Critical

Knowledge Repository Component.

CKR Component – Knowledge fields

Document Repository Management

Information Security – File Encryption

3.4 The Alert and Log Component

The alert and log component requisites are: alerting

the user by email of the probability of the document

containing evidences about a child abuse; and

each document for further analysis. The system

should select what cases should trigger an alert or

intelligence classification algorithms very well

tested and implemented to assess the evidences and

give value to the sensibility of the document in this

context.

Table 4: Knowledge Science fields used in Alert and Log

Component.

AL Component – Knowledge fields

Artificial Intelligence – Classification algorithms

Information and Communication Technology

4 RESEARCH RESULTS

With the precious help of the Young and Child

protection Committee of Fafe, we could have

accessed to 16 anonymized processes of child

abuses. The goal of this committee is to decide

whenever they should apply the protection measure

of the child, based, mostly, on the accusation report,

medical reports, teachers and school reports and

diligences. Ten of the processes refer to cases in the

final stage, so we used them to train the Child Abuse

Critical Knowledge Monitoring System. The other

six processes were in early stages and we’ve used to

validate the accuracy of the system for the detection

of a child abuse case to the prediction of the measure

to adopt. All of the six cases were detected as child

abuse cases. Two of them, the system considered

that they don’t need child protection with a certainty

of 99% - Group A. Another two, the system

considered that should have a child protection

measure with certainty of 99% - Group B. Finally,

the last two the system considered that should have

the measure with certainty of 70% and 89% - Group

C. From the perspective of the committee of the

group A, independently of the system

considerations, they don’t need the child protection

measure. For the group B, the committee already

have implemented the measure. Finally, for the

group C, the committee considered that they

shouldn’t have the measure of child protection.

We’ve communicated this results to the committee

and one and a half month, later, they reply to us. In

the case of 89% of certainty of the system, the

committee, clearly, decided to apply the child

protection measure. For the 70% case of certainty

they apply the measure but, because it has been

changed family/home context of the child that lead

to that conclusion.

4.1 Results Validation

The system was validated in three stages:

Knowledge Security - An Empirical Use of IT – Child Abuse Monitor System Model

239

Laboratorial context, Real context as stated in the

previous paragraph. Since we developed the

prototype with the knowledge engineering and

management body of knowledge we have validated

the system with the matrix of quality dimensions of

knowledge management and knowledge

management system (Pereira and Santos, 2013b).

4.1.1 Laboratory Context

The objectives of laboratory context were validating

the system requisites and systemic function of all

components. In this, were used documents in other

areas beside child abuse to validate false positives.

And documents of the theme child abuses to validate

the true positives.

Table 5: Child Abuse process characteristics - Final Stage.

Process

Words

(OCR)

Pages

Protection

Measure

Court

Decision

CPCJ1

2543

Não

CPCJ2

1373

CPCJ3

3725

Yes

CPCJ4

1352

Yes

CPCJ5

604

CPCJ6

918

CPCJ7

1664

Yes

CPCJ8

1433

CPCJ9

1397

Yes

CPCJ10

781

Total

15790

4.1.2 Real Context

In the validation on real context the processes of

child abuses that we had access have these

characteristics, see tables 5 and 6:

Table 6: Child Abuse process characteristics - Earlier

Stages.

Process

Words

(OCR)

Pages

Protection

measure

Court

Decision

CPCJ11

128

Indefined

CPCJ12

932

Indefined

CPCJ13

694

Indefined

CPCJ14

340

Yes

CPCJ15

249

Indefined

CPCJ16

842

Yes

Total

15790

4.1.2 The Matrix of Quality Dimensions of

Knowledge Management and

Knowledge Management Systems

The matrix of quality dimensions of knowledge

management and knowledge management systems

allows us to look to our management knowledge

system on three different dimensions: System, see

tables 7, 8 and 9, User, see table 10 and

Organization and perform an auto critic analysis.

Since this is a prototype and not a final product with

broad implementation, we cannot evaluate the

organization dimension that addresses subjects, such

as: Organizational support, Perceived usefulness of

knowledge Sharing/ Intent to use/ Perceived benefit,

Knowledge/System/Net benefits, Knowledge /

Information quality and service quality.

Table 7: System Quality Dimension.

System Quality

Stability

The lab experiment

allows to see that the

documents output is the

same in different times of

analysis and it kept on

real documents.

Response time

The system has the

capacity of processing

273 words per second and

1,11 seconds per page.

User-friendly and ease of

use

Since this is a prototype

this aspect is less treated

in this research.

Knowledge Classification

The system is capable of

relevant knowledge

recognition in documents

of the target subject.

Technical resources

According to knowledge

management this

prototype is capable of

capture knowledge from a

variety of document

types, identifies

knowledge addressing an

ontology and applies

algorithms of

classification to validate

the identified knowledge.

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Table 8: Ontology Quality.

Ontology Quality

Accuracy and Correctness

The ontology was based in

child abuses regulation and

procedures of medical staff

(Saúde, 2008).

Authority

Besides it was based on

existing HL7 ontology, the

child abuse ontology it’s our

authorship.

Clarity

All the ontology concepts are

identified and supported by

regulation.

Interpretability

The concepts mapped on this

ontology are from Healthcare

area, since the users are from

healthcare to the

interpretability facilitated.

Completeness/Coverage

All the concepts of child abuse

mentioned in regulation were

mapped on this ontology.

Consistency

There’s no ambiguity in the

concepts although, the

ontology is multiple language

(English and Portuguese).

History

The ontology is based on an

HL7 ontology and is suitable

to be accoupled to it.

Infrastructure

The ontology infrastructure is

scanned, automatically, by the

prototype so its definition is

correct.

Knowledge Sharing

The ontology is OWL

compliant so it can be used and

updated by others.

Lawfulness

The ontology is based on

regulation and it preserves the

privacy by technical

implementation.

Metadata Evolution

The ontology is OWL

compliant so it can be

extended with other

ontologies.

Minimality

The concepts of the ontology

are reduced to its atomicity.

Purpose

The ontology achieves

completely the objectives.

Relevance

This ontology its fundamental

to the functionality of the

prototype.

Richness

The ontology addresses the

concepts and its relation so it’s

more valuable than dictionaries

or bags of words.

Security

The ontology editor allows us

to control the access to the

ontology, although, in this

research it’s not addressed.

Strategy

This ontology is in a standard

format so it could be updated

as the theme evolves.

Traceability

The ontology editor allows us

to control the versions of the

ontology and restore them,

although, in this research it’s

not addressed.

Table 9: Knowledge Retainer Quality.

Knowledge Retainer Quality

Accuracy

The repository component is

based on opensource software,

MongoDB. It’s a repository

oriented for documents that’s is

appropriated to distributed

systems with replication technics

and unique identifier of

documents.

Authority

Since it’s opensourced there is

no problem about authorship.

Expertise

This is a very specific software

to management documents with

scalability. This was the main

issue that lead us to choose it.

Consistency

The repository by redundancy

assures high consistency to

documents access.

Credibility

This software has the maturity of

10 years since it was developed

in 2007 and largely used.

Degree of detail

For each document, it’s possible

to include Metadata allowing us

to add its characteristics and

notations.

History

The MongoDB has implemented

a control version that allows us

to access previous versions

Reuse

The prototype allows us to

process again documents by

option.

Relevance

This tool is essential for the

performance of this prototype

and possible adaption to bigdata

systems.

Sharing usefulness

Since this tools is prepared to

distributed systems context it

allows multiple users with high

performance.

Degree of context

Since it’s a document repository

allows to store and retrieve all

the documents produced in the

analysis process of the system.

Accessibility

The characteristics of this tool

defines an high availability to

documents.

Degree of socialisation

Since this prototype deals with

restricted access documents –

critical knowledge this variable

doesn’t apply to it.

Security

All the security mechanisms,

such as: access control, SSL,

among others are possible to

implement on this system.

Willingness to share

Since this prototype deals with

restricted access documents –

critical knowledge this variable

doesn’t apply to it.

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241

Table 10: User Dimension.

User Dimension

Job performance

This prototype could increase

de efficiency on the

identification of child abuse

cases and promote faster the

child protection measure.

Productivity, Easefulness

tasks and system

The results achieved with this

system allows us to interpret

that directly the productivity

could be increased and we

could alert child abuses cases

that need to be further

analysed.

Knowledge that meets the

needs

This prototype is very

specific to healthcare area,

but it’s possible to ply it to

any area of knowledge and

any language support.

Content

The prototype describes the

relevant evidences presented

in child abuse process.

Accuracy

With the available results and

without a broad application

the prototype presents us

good quality in child abuses

cases identification.

Format

The prototype was developed

in a programming language

that could be used in all

operating systems.

Ease of use and timeless

Since this is a prototype the

easiness of use was not the

principle concerned to us.

Although, we can consider

that the time of the all

process has good

performance.

5 CONCLUSIONS

Beside the small sample that we have access to, by

the kind collaboration of young and child protection

Committee (YCPC), we could achieve these results:

from sixteen child abuses processes documents, all

of them were identified by the system as pertinent to

child abuses context. Ten of the processes had

complete all phases of the YCPC analysis and they

were used in training of the system in the model

definition and the other six that were in the

preliminary stages of the YCPC analysis, were used

as a test of the model. As a final result, the system

was capable of identify two processes as critical in

order to apply the child protection and the same

processes had that measure applied by YCPC. Two

others were not considered by the system and by the

YCPC as critical to use the child protection measure

and the last two processes were considered critical

by the system but not yet been classified by YCPC.

Based on these results, it’s possible to conclude that

with the definition of the sensible topics in health

care, with the use of laws and regulations and health

care terminology promote the identification of the

sensible concepts to develop an ontology about child

abuse cases critical knowledge that objectively

address the research question - Can we automatically

classify health care information as critical

concerning laws and regulations, terms and

knowledge sensibility in order to preserve it? Thru

knowledge sensibility it was possible to get the

critical documents and the prediction of the use of

the child protection measure that is the main goal of

the organization - YCPC. The ontology of the health

care critical knowledge, the critical knowledge

monitor system (prototype) and the knowledge

sensitivity applied to knowledge security are the

contributes of this research.

Since this prototype is generic, as long as we

apply to it other ontologies and objectives, we could

address other aspects of Health Care Sensitivity,

such as: Adults Abuses, Substance Abuse, Genetic

Diseases, HIV/AIDS, Psychiatry, Sexuality and

Reproductive, Sickle Cells, Sexual Transmitted

Diseases and Taboo, identified by HL7 initiative as

Health Care Sensibility areas. Technically speaking,

the prototype it is prepared to apply to it “BigData”

systems, since the repository is a Document Based

already used in this kind of systems and the Capture

component and Ontology Component could be the

MAP and The Alert and log Component could be the

Reduce part of the MapReduce process of “BigData”

or even, we could plugin it to Electronic Health

Records System and monitor these Health Care

aspects.

ACKNOWLEDGEMENTS

This work is financed by FEDER funds through the

Competitive Factors Operational Program –

COMPETE and Portuguese national funds through

FCT – Fundação para a ciência e tecnologia in

project FCOMP-01-0124-FEDER-022674.

This work was conducted using the Protégé

resource, which is supported by grant GM10331601

from the National Institute of General Medical

Sciences of the United States National Institutes of

Health.

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