=Paper=
{{Paper
|id=Vol-2306/paper3
|storemode=property
|title=A study of Information System Mutations (Changes) Using Knowledge Organization System (KOS) Applied to Biomedical Research Study
|pdfUrl=https://ceur-ws.org/Vol-2306/paper3.pdf
|volume=Vol-2306
|authors=Amel Raboudi
|dblpUrl=https://dblp.org/rec/conf/ekaw/Raboudi18
}}
==A study of Information System Mutations (Changes) Using Knowledge Organization System (KOS) Applied to Biomedical Research Study==
https://ceur-ws.org/Vol-2306/paper3.pdf
A study of Information System mutations (changes) using
Knowledge Organization System (KOS) applied to
biomedical research study
Amel Raboudi1,2,3
1 FEALINX, 37 rue Adam Ledoux 92400 Courbevoie, France
2 INSERM, UMR970, Paris-Cardiovascular Research Center at HEGP, Paris, France
3
Université de Technologie de Compiègne (UTC), UMR 7337 Roberval, Compiègne, France
amel.raboudi@utc.fr
Abstract. A biomedical research study is a collaboration process where several
experts, institutions, disciplines and data sources are involved. Traceability of
data provenance and efficient data management are essential in order to guarantee
results integrity. Researchers, however, use different Information Systems (IS) in
order to collect, process and analyze their increasingly complex datasets. Accord-
ingly, Research Data Management (RDM) is seen as a tedious, time consuming
and error prone task. In a previous work, an IS based on Product Lifecycle Man-
agement (PLM) technology was proposed to manage data heterogeneity and
provenance throughout the biomedical research lifecycle. However, the fast-
changing context of biomedical research causes data, information and knowledge
changes, that we hereby call mutations. Mutations can affect IS components and
impact IS consistency. In a collaboration context, it is important to have the same
shared knowledge for all actors. Therefore, the use of a Knowledge Organization
Systems (KOS) is proposed in order to model shared knowledge and enable deal-
ing with knowledge level mutations.
Keywords: Information System, Knowledge Organization System, Ontology
evolution, Research Data Management, Change Management, Mutation.
1 Introduction
Biomedical studies are multisource, multidisciplinary, multimodal, multi-partners and
include longitudinal series. Biomedical researchers are constantly moving back and
forth from one data source to another, in order to collect, curate, process and analyze
heterogenous data. Therefore, biomedical Research Data Management (RDM) is par-
ticularly a complex, time consuming and error prone task. Issues about research repro-
ducibility, data provenance, data sharing, data interoperability and reuse are major in
biomedical research field. Solutions such as Information Systems (IS), experts, accurate
methods and processes are essential to data management for biomedical RDM. Bio-
medical research labs rarely use intensive processes and methods for RDM; in most
2
cases lab investment in that field is negligible: In 2016, European Union decided for
the first time to allocate 5% of project budget to RDM [6].
Several ISs are used for biomedical RDM, each one is dedicated to a type of data:
Laboratory Information Management System (LIMS) [8] for biological samples infor-
mation; PACS (Picture Archiving and Communication System) and RIS (Radiology
Information System) for imaging data, etc. They are either domain specific or data for-
mat centric or both. Besides, they don’t cover all aspects and steps of a biomedical
research study. In a previous work, an IS based on Product Lifecycle Management
(PLM) technology: Biomedical PLM [1] was proposed to manage heterogeneous data
provenance. It aims at managing all types of biomedical data throughout the biomedical
research lifecycle: (1) specification, (2) acquisition of raw data, (3) processing of de-
rived data, and (4) scientific publication. Biomedical PLM is a study centric, lifecycle-
oriented data management system, that enables sharing among actors, processes, or-
ganizations and distant sites.
The context of our work is the DRIVE-SPC1 project. Its goal is to develop an inte-
grated solution to manage biomedical research data of the Imaging Research Labora-
tory (LRI) - team 2 - at Paris Cardiovascular Research Center (PARCC). Data used by
LRI are mainly preclinical imaging exam results for oncology and cardiology research:
PET-CT, MRI, ultrasound, and histology. A biomedical PLM instance is currently de-
ployed for the lab members in order to manage data of several studies: cardiotoxicity
of a cancer treatment, tumors metabolism, etc.
The fast-changing context of biomedical research is an additional issue for data shar-
ing among the laboratory members. For instance, a researcher changes the way he or-
ganizes his data without informing data users, a change in the measurement unit of a
crucial parameter for analysis after software update, internal changes in a partner labor-
atory that affect project schedule and eventually data, discovery in science that must be
taken into consideration, etc. Observations done on the LRI lab reveal that previously
listed unexpected changes can have serious consequences on the IS and the research
project. For instance, limited access for a period of time, errors in automatic
toolkits/routines, loss of data, inconsistency of information, loss of time, issues about
results integrity, impossibility of data sharing and loss of data provenance etc. All these
events have in common data and information changes (lost or gain) that were not an-
ticipated by the IS original design. Our hypothesis is that unexpected changes are
mainly related to knowledge sharing issues, because motivation behind unexpected
changes exists in people minds and organizations memory. Therefore, changes cannot
be studied without an accurate modeling of the shared knowledge among all IS actors
(users/developers).
The research problem addressed in this article is how to manage unexpected changes
in an IS (applied to biomedical PLM) in order to guarantee its consistency and contin-
uous usability, taking into consideration the fast-changing context of biomedical re-
search and the variable lifespans of different IS components and partners. First, a liter-
ature review is presented. Then, we focus on our proposed preliminary approach to
manage IS changes, and finally our research methodology and discussion are drawn.
1 a collaboration between Fealinx company and LRI, financed by USPC university.
3
2 State of the art
Data-Information-Knowledge-Wisdom (DIKW) framework for IS research highlights
the importance of knowledge: Knowledge is data and/or information that have been
organized and processed to convey understanding, experience, accumulated learning,
and expertise as they apply to a current problem or activity [20]. IS definition at [4]
describes two parts of an IS: the known part since the IS design phase, and the unex-
pected part, that depends on all other systems (technical/social) composing the IS en-
vironment. In Information Systems, data and information are explicitly managed, but,
knowledge is implicit which prevents unexpected changes to be properly managed. To
make knowledge explicit among actors, and therefore manage the impact of knowledge
changes on IS, we must look for a way to manage knowledge.
2.1 Knowledge Organization System (KOS)
Knowledge Organization Systems (KOS) are designed to manage knowledge: KOS are
defined as all types of schemes for organizing information and promoting knowledge
managements [12]. KOS includes classification schema, standardized terminology,
structured vocabulary, glossaries, semantic networks, ontologies, etc.
Knowledge Organization System and IS. Managing the knowledge level with KOS
is an important step in the process of ensuring IS consistency: The work on [2] proposed
and validated CoMIS-KMS, a process to guide the conversion of an existing Infor-
mation System to a knowledge management system with the use of a knowledge base
(KOS). Haase PhD [10] proved that ontology (KOS) evolution allows to manage
changes in distributed ISs in a consistent manner.
Knowledge Organization System evolution. Dos Reis et al. [5] studied KOS mapping
maintenance issues after KOS evolution in the biomedical field. Studied KOSs are:
NCIT (Thesaurus), ICD-9-CM (Classification), SCT (Ontology) and MedDRA (Dic-
tionary). General KOS evolution is rarely addressed in the literature, instead, the evo-
lution study of certain types of KOS exists and especially ontology [10] [16] [19] [21]
[22] [24].
2.2 Ontology evolution
A KOS classification proposed by [3] underlines that ontologies are the most semanti-
cally clear type of KOS. Ontology is an explicit specification of a shared conceptual-
ization of a domain of interest [9]. Ontology change is a largely addressed field in the
literature. An exhaustive analysis is presented in [7] where ten subareas for ontology
change research fields are defined together with their mutual boundaries. Some of them
deals with heterogeneity resolution between ontologies as a proposed method to change
an ontology, through ontology mapping and matching [24]. Another concern is about
ontology fusion (integration and merging) issues [21] [10]. The same article also
4
presents ontology evolution and separates it from ontology debugging. Both research
fields focus on incorporating changes in an existing ontology while avoiding incon-
sistency.
A state of the art at [23] presents ontology evolution as a five step cycle as follow:
(1) Detecting the need for evolution, (2) Suggesting changes, (3) Validating changes,
(4) Assessing evolution impact, (5) Managing changes: (5.a) Recording changes and
(5.b) Versioning. For each step, a review of related literature is presented. Briefly, (1)
is the starting point for ontology evolution process by detecting a need for change that
can be initiated from user behavior [19] or data sources [22]. (2) is the phase of change
representation with the help of structured or unstructured resources, for example online
ontologies were used by [22] as a background knowledge for integrating newly discov-
ered concepts. (3) assesses the relevance of the suggested change to the domain and its
impact on ontology consistency. (4) treats impact on dependent applications and exter-
nal artifacts that uses the ontology under change. (5) applies and traces the change
throughout ontology versions. An example is in [14], it applies provenance W3C stand-
ard [15] in order to trace ontology changes.
3 Proposed approach
Our approach consists of two main propositions: first, we develop an analogy between
IS unexpected changes and mutations in genetics, and second, we propose a model for
shared knowledge among IS actors in order to manage unexpected changes. Both are
in their preliminary steps.
Expected and unexpected change. Based on IS definition at [4], we propose to con-
sider two types of changes: expected and unexpected changes. Expected changes are
managed according to a defined and established process of change management and IS
evolution: identifying a need for change, scheduling the change operation and execut-
ing it. Unexpected changes cannot be managed through a regular process. They can be:
an IS dependency change, a wrong use of an IS functionality, external systems software
update, etc. We focus on unexpected changes as they have serious effects on systems
consistency and usability such as data sharing and data interoperability issues.
Unexpected changes and mutation. We propose to consider unexpected changes as
mutations. A mutation in genetics is a sudden change in DNA code that continuously
occurs in bio-cells and that is fundamental for species evolution with regard to natural
selection [13]. In IS research or in genetics, an evolution process for an object (thing)
is a migration from a consistent state A to a consistent state B. The complete analogy
with genetics is not treated in this article but it will be addressed in the course of this
PhD. IS mutations can affect any IS component and can lead to an IS evolution (best
case scenario) or an IS inconsistency otherwise.
5
DIKW and Knowledge engineering. We choose the DIKW framework to consider IS
mutation concept. Mutations in DIKW framework can occur at each level: data muta-
tion, information mutation, and knowledge mutation. Observations done at the LRI lab
revealed that available data and information in the IS do not give any indication about
knowledge involved. Besides, knowledge mutations have their origin in external
knowledge sources and Information System technical and social environment. Thus,
we propose to study IS mutations with the use of knowledge engineering in order to
focus on knowledge mutations. Our aim is to ensure knowledge sharing among all ac-
tors. Then, we propose to study knowledge evolution and link it to IS evolution in order
to manage mutations phenomenon in all DIKW levels. To this end, we must deal with
two challenges. First, lab data integration in Biomedical PLM for IS usability enhance-
ment. Therefore, we proposed a generic data integration method that allows different
research data types to be imported in Biomedical PLM [17] [18]. And second, modeling
Biomedical PLM knowledge taking into consideration change management and muta-
tions issues, which is a work in progress.
Knowledge Organization System (KOS) for Biomedical PLM. IS manages explic-
itly data and information levels, whereas Knowledge Organization Systems (KOS)
manages knowledge level. We propose to manage knowledge mutations with the help
of a Biomedical PLM KOS. As presented in the state-of-the-art, ontologies are the
most semantically clear type of KOS. The proposed biomedical PLM KOS is based on
an ontology that models the knowledge of (1) the Biomedical PLM Information System
and (2) the shared knowledge of biomedical research studies: general biomedical on-
tologies, domain ontologies, laboratory vocabulary, etc.
4 Methodology
The aim of this research is to manage unexpected changes, that we call mutations, in
Information Systems in order to ensure IS consistency and continuous usability.
Key steps of our work methodology are (1) exploration of the DRIVE-SPC project
context and identification of unexpected changes, (2) state-of-the-art of IS changes re-
lated literature, (3) development of biomedical PLM KOS for biomedical RDM with
the use of ontologies, (4) proposition and modeling of the whole process of managing
mutations in IS with the use of KOS in biomedical research study and (5) test and val-
idation of the proposed approach according to DRIVE-SPC project context.
Our first PhD year focused on the deployment of the Biomedical PLM IS in LRI lab
in order to identify mutations. It aims to increase usability of Biomedical PLM in the
lab in order to track every type of mutations. Therefore, data of a pilot research study
were integrated retrospectively in the IS in order to give a real-life use case for research-
ers [20]. And recently, some speed-interviews sessions with five key users were orga-
nized in order to identify more relevant use cases scenarios.
Presently, ontology representing Biomedical PLM knowledge is under construction.
Exploration of available methodologies in literature for ontology construction and in-
tegration, together with ontology development framework choice (Protégé, NEON
6
Toolkit)2 and language selection (OWL Lite, OWL DL, SKOS, RDFS) are some of the
ongoing work.
Next, matching and mapping between (1) Biomedical PLM ontology, and (2) Bio-
medical PLM Information System, and traceability of mutations in the whole system
will be developed.
5 Discussion
In this article, we introduced Information System mutation concept (unexpected
change) based on a preliminary analogy with genetic mutations, and we proposed an
approach to analyze Information System mutations with the help of Knowledge Organ-
ization System (KOS). It aims to provide a shared reference for IS actors and then fa-
cilitate mutations management. In order to succeed the shared knowledge design, a fur-
ther literature review must be done concerning ontology integration, mapping and
matching, together with ontology versioning, evolution and debugging.
Actors involvement. IS needs to maintain up-to-date functionalities despite the fast-
changing context of biomedical research studies and the multiple partners involved.
Our methodology involves partners from an early stage. This is done through inter-
views, data management behaviors tracking, data preparation, etc. Our proposition de-
pends on IS actors reactivity and understanding of knowledge acquisition importance.
This is a risk factor to consider in our research.
System modeling. With the design of Biomedical PLM KOS, a cover of DIKW levels
mutations is assured and a larger vision of system changes is provided, which offers a
comprehensive framework for IS mutations management. However, this choice adds
complexity upon the Biomedical PLM system. Thus, it is interesting to consider a
model for the whole system (IS+KOS) to better clarify the role, boundaries and impact
of each one.
IS customization. When a mutation occurs, IS providers are supposed to act rapidly in
order to manage mutations and ensure continuous IS relevance to customer’s need. Hal-
ler [11] describes the dilemma of IS providers “in-between” the management of all IS
changes and the reduction of IS customization related costs. This is an interesting re-
quirement to consider while proposing mutation management strategy.
2 https://www.w3.org/wiki/Ontology_editors
7
Acknowledgments
The author wants to thank her direct supervisor at Fealinx Company Dr. Marianne Al-
lanic. Her PhD directors Pr. Bertrand Tavitian and Pr. Benoît Eynard. Her supervisors
Dr. Alexandre Durupt (UTC), Dr. Philippe Boutinaud (Fealinx), and DRIVE-SPC pro-
ject colleagues: Dr. Pierre-Yves Hervé (Fealinx) and Dr. Daniel Balvay (Inserm). Spe-
cial thanks to LRI key users: Dr. Thomas Viel, Anais Certain, Thulaciga Yoganathan,
and Caterina Facchin for their time and help.
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