While the essential knowledge domain for ship-operation is large and growing rapidly, and the available time for the proper training of maritime professionals is perhaps shrinking to meet the growing industry demand, it is becoming more & more essential to ensure proper capture and management of this important knowledge-base. Poor management of this knowledge area may not only result in gaps in training but breaches of safety in critical shipboard procedures and perhaps further aggravated by the attrition of trained shipboard personnel as they move to work ashore. In the paper, the authors will address these issues and describe a possible dynamic digital knowledge-capture strategy, and the development of an incrementally growing maritime digital knowledge repository, which could not only alleviate some of these problems but lead to an overall improvement in ship safety and operations. The authors will also share the methodology, presently being planned at the Malaysian Maritime Academy (MMA), and which is likely to lead to avenues of collaborative work between MMA, academia (UTM) and shipping companies.

1. Towards A Digital Repository of Ship-Operations:
The Capture and Management of a Knowledge-base
for Safety & Education
Kalyan Chatterjea1, Rose Alinda2, Pradeep Chandrasen Nadkar3
1 Head, Research & Consultancy, Malaysian Maritime Academy, Malacca, Malaysia [kchatterjea@alam.edu.my]
2 Professor Computer Science, Dean, Graduate School (Engineering), University Technology Malaysia [alinda@utm.my]
3 Director, Training & Education, Malaysian Maritime Academy, Malacca, Malaysia [kchatterjea@alam.edu.my]
Abstract:
While the essential knowledge domain for ship-operation is large and growing rapidly, and the available time
for the proper training of maritime professionals is perhaps shrinking to meet the growing industry demand, it
is becoming more & more essential to ensure proper capture and management of this important knowledge-
base. Poor management of this knowledge area may not only result in gaps in training but breaches of safety
in critical shipboard procedures and perhaps further aggravated by the attrition of trained shipboard
personnel as they move to work ashore. In the paper, the authors will address these issues and describe a
possible dynamic digital knowledge-capture strategy, and the development of an incrementally growing
maritime digital knowledge repository, which could not only alleviate some of these problems but lead to an
overall improvement in ship safety and operations. The authors will also share the methodology, presently
being planned at the Malaysian Maritime Academy (MMA), and which is likely to lead to avenues of
collaborative work between MMA, academia (UTM) and shipping companies.
[Key Words: Knowledge-capture, digital repository, knowledge-base, concept map, collaborative ontology,
student-centered learning, work-integrated learning, information visualization, knowledge
visualization, domain ontology and task ontology.]
1. Introduction
Shipboard technology and work procedures are changing rapidly just as the work practices
ashore. Managing teaching and learning resources at Maritime Education and Training
(MET) Institutes are becoming more challenging.
In previous decades, MET lecturers could fall back on their own shipboard experiences.
Today they find themselves lacking in the areas of procedural knowledge and also on
knowledge of the latest equipment on board. The books, which used to be good sources of
information are dated and cannot provide the required knowledge sought by ships’
operators.
While referring to traditional training in MET Institutes, Dr Barry Strauch of US National
Transportation Safety Board, said In the US Merchant Marine Academy (USMMA) Seminar
on Bridge Resource Management (Strauch, 2008)
“Traditional training primarily:
• Ensures familiarity
• Overlooks proficiency
• Overlooks most challenges presented by highly automated systems”
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2. At the same conference, Captain George Sandberg, Professor and Director of Nautical
Science Simulation at the US Merchant Marine Academy questioned the efficacies of
introducing advanced navigational equipment on the bridge without relevant training.
"Advanced navigation systems are being fitted and used aboard ships at a rate faster than best
practices are established. Many ships today are using ECDIS routinely, but the best practice for the
use of that equipment has not really been established. Do the majority of today's mariners know how
to use this new equipment effectively or correctly? And, of even more concern, due to self-teaching,
do they really understand the capabilities of the equipment, or do they have misunderstandings of
how to use it?"
Recognizing that in today’s shipboard practice, procedural changes are very dynamic, it
becomes evident that MET institutions require constant updating and revision of knowledge
delivery using technology in order to keep pace and remain relevant in the fast changing
maritime industry.
For such a pursuit, the stakeholders, namely, the MET Institutions, the Shipping Companies,
Academia (specializing in technology for knowledge management) and most importantly,
the Learners should be involved in a collaborative agreement to synergize the project
processes.
In this paper, we share some of our plans at the Malaysian Maritime Academy for acquiring
this important knowledge, as it is practiced on board. This is perceived to be made up of the
knowledge about the (1) equipment incorporating new technology as well as the (2) best
practices for safe operation of systems incorporating such technology.
The paper would attempt to describe the proposed knowledge capture methodologies and
the depiction of the captured knowledgebase for
• quick accessing
• use in teaching, learning and
• dynamic updating
2. Strategy for Knowledge-capture
2.1 Knowledge-capture Arena
“...knowledge and learning occur within a domain of practice, within a world view, within a
context. The process of acquiring knowledge is not like a transfer, as though knowledge were
a set of blocks, but a process of immersion, as through knowledge were a body of water.”
[Downes, 2004]
Serova (2009) revealed findings at Intel, when they studied contributions of various learning
sources towards actual productivity at the workplace. Fig. 1 shows the findings, which
attributes only 10% to formal institutionalized learning, 20% to networking and the major
learning through one’s own experience on the job. Other studies also endorse learning-to-
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3. knowing transformation in the “ambient community of practice” (Lave & Wenger, 1991;
Chia-an and Yin, 2003).
Hence, capturing through work-integrated leaning appears lucrative, especially in maritime,
where learners frequently revisit MET institutions to fulfill their STCW requirements. This
allows for a shipboard knowledge-capture arena (see Figure 1.), which could function as a
knowledge-lab for learners. Given the right assignments and adequate guidance, the learner
could help populate and update the institutional knowledgebase with regular update cycles.
Shipboard
Work-integrated
Learning &
Knowledge-capture
Figure 1 Planned Knowledge-capture Arena is based on the findings of Intel Research (Source: Serova, 2009)
2.1 Knowledge-capture Methodology
“...content – whether in the form of textbooks or in the form of learning objects – can never
be at the centre of a learning environment, but may only form a part of the surround. They
way people learn, quite literally, is to swim in knowledge, and the way to swim, is to have a
direction to swim to. Learning communities provide that direction, and a new learner is, as it
were, swept up with the school as it swims in a certain way, in a certain direction.” [Downes,
2004]
Referring to knowledge-capture, Stephen Downes (ibid.) says that it “consists of being able
to capture those artifacts, of being able to recognize the salient features of the context of
transmission, of being able to store them in digital form, and of being able to recognize new
contexts in which the same transmission would be of value. Though this sounds daunting, it
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4. has already been accomplished in simple form; the trick is to make it work with the
complexity and richness of actual human communication.”
To simplify the shipboard experience transfer to the MET Institute, we plan to build a
domain ontology, which is less formal (not directly machine-readable), meaning, having
more human-like semantics. An ontology is an explicit specification of a conceptualization
(Gruber, 1993). In a community driven ontology building, complexities of formal
development can create barriers to the development process. While, in a less formal
process, there is faster progression and the number of participants can be scaled up (Hepp
et al., 2006; de Moor et al., 2006). Hence, such a system can lend itself towards higher
reuse.
To develop a community driven ontology, we plan to use concept maps. Concept maps are
frequently used for knowledge elicitation and visual depiction of knowledge (Castro et al.
2006; Allert et al. 2006; Van Damme et al. 2007). Concept maps describe a domain using simple
graphical representation of concepts, which are shown as nodes and the relationships
between these concepts are sown as arcs. A unit of knowledge is sometimes referred to as a
triplet (or a proposition) and consists of at least two concepts (or nodes) and a relationship
(an arc with some meaningful phrase). Propositions contain two or more concepts connected
using linking words or phrases to form a meaningful statement (Novak & Cañas, 2008). To
develop more complex relationships more triplets could be added and to represent a large
domain, many such maps could be integrated. Additionally, the nodes and relationships can
be provided with topical resources to enhance richness of the knowledge representation
(Chatterjea and Nakazawa, 2007).
Details of community driven maritime ontology development (Chatterjea, 2006; Chatterjea
and Nakazawa, 2008) was reported earlier and a longitudinal study could now be
undertaken to exploit the potential of this platform. The next sections elaborate the
strengths of information and knowledge visualizations, which will be an integral part of the
ontology development.
2.2 Information and Knowledge visualization
“Information visualization has been a research topic for many years, leading to a mature field
where guidelines and practices are well established. Knowledge visualization, in contrast, is a
relatively new area of research that has received more attention recently due to the interest
from the business community in Knowledge Management.”[ Cañas et al. 2005]
Burkhard and Meier (2004) defined knowledge visualization as the use of visual
representations to transfer knowledge between at least two persons. Burkhard (2005) also
differentiated information and knowledge visualization, which is depicted in Figure 2.
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5. Figure 2 Information and knowledge visualization: Burkhard’s ( 2005) differentiation with modification
Burkhard and Meier (2004) further developed a Knowledge Visualization Framework
consisting of four perspectives that need to be considered when creating visual
representations that aim to transfer and create knowledge. The framework is shown in
Figure 2.
Figure 3 Knowledge Visualization Framework (Modified from: Burkhard and Meier, 2004)
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6. An example of knowledge visualization is shown in Fig. 4 below. It provides an advanced
user interface for training support, where the deconstruction of a major task of activating an
LNG tanker from the dry dock to full-away conditions using such an interface is shown. The
nodes in the map are embedded with learning resources. The numbers in figure (1), (2)..(20)
refer to the simulation exercises. It is expected that as the learners go through these
sequential tasks on the steam-propulsion simulator, they would access the resources and
thereby pick up the essential knowledge and proficiency required for the task at-hand. They
will also be able to assess themselves using the built-in e-assessment components,
embedded in this advanced learning organiser. Localisation of knowledge using concept
mapping tools can provide resources for self-regulated resource-based learning (Tergan and
Haller, 2003).
Figure 4 Example of Knowledge Visualization: Advanced Knowledge Organiser for use in Simulation-based
Learning (Source: IMO STCW Revision Document STW 39/7/19 – 2007)
Concept mapping tools support situational relevance on spatial representations, which
could breakdown complex learning tasks into manageable learning objects, with their own
resources. Graphical representations in concept maps enhance cognitive process of
managing knowledge and information in resource-based learning and problem solving
environments.
2.3 Domain and Task Ontology
Ontological analysis clarifies the structure of knowledge. The first reason is that they form
the heart of any system of knowledge representation. If we do not have the
conceptualizations that underlie knowledge, then we do not have a vocabulary for
representing knowledge. Thus the first step in knowledge representation is performing an
effective ontological analysis of some field of knowledge. (Chandrasekaran et al., 1998)
A domain ontology is a formalisation of the knowledge in a subject area (domain) such
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7. as stability, navigation, thermodynamics etc. This could be considered as static knowledge
from a particular perspective and is frequently referred to as declarative and classificatory
(Ferguson-Hessler and de Jong, 1990; Cañas and Novak, 2006). It is usually arranged
hierarchically (see example in Figure 5).
Domain ontology differs from other types of ontology such as the task ontology (a
formalisation of the knowledge necessary to solve a specific problem or task e.g. warming
up the propulsion engine, changing over fuel or making fast of the vessel after arriving port).
This type of knowledge is procedural, could be evolving or considered to be dynamic in
nature (see example in Figure. 4) and is referred to as cyclic or sequential knowledge
(Safayeni, et al., 2005).
Figure 5 Declarative or classificatory knowledge: domain ontology of steam turbines /boiler protection
The differentiation, described above, conforms to maritime practice, where we differentiate
between knowledge (e.g. knowledge of thermodynamics or stability) and skill (e.g. skill/
proficiency of operating a steam boiler or interpreting ship’s radar-plotting). Hence, we
could organized the knowledgebase into two distinct areas and call them (1) knowledge,
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8. which is meant to be classificatory, defining the basic concepts of the domain and (2)
proficiency which is explanatory, more dynamic in nature and could be procedural tacit
knowledge of experts. The classificatory knowledge could be referred to as being object-
based and proficiency to be event-based (Cañas and Novak, 2006).
Figure 6 Steps and Milestones suggested by Castro et al. (2006)
Steps and milestones for the project for developing the concept-map-based knowledge are
to follow the guideline given by Castro et al. (2006). The main challenge for the project
would be to develop the items 1, 2 and 3. Ontology refinement and formalisation efforts will
be undertaken at a much later stage as the tools for these processes are still evolving (see
details at http://coe.ihmc.us/groups/coe/). So, when these tools are matured, we could
progress with the remaining stages, which will make the searching processes much more
efficient.
2.4 Knowledge capture at sea
Perhaps, the easiest channel of capturing updated operational knowledge from the
practitioners is when our engineers/officers go to sea for their sea-time and get exposed to
both domain ontology as well as the procedural knowledge as practiced on board.
Appropriate assignments should be given to the engineers and officers to capture these
ontologies. The process will ensure currency of both types of the ontologies discussed
earlier and as the engineers/ officers are placed on various ships, the different case-studies
captured will enrich the knowledge-base with adequate diversity. Thus, the trainee
officers/engineers would be asked to develop their portfolios of organised knowledge,
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9. which could be vetted by seniors on the ships and by the facilitators at the MET Institute,
before these are allowed to populate a digital repository at the Institute.
3. A Digital Repository
Visual ontologies with resources attached to nodes lend themselves as efficient knowledge
organisers and are reported to be suitable for digital libraries or repositories with text-based
searching facility. Concept maps created by learners serve as summarization of a domain. In
a digital repository learning environment, it is claimed, that concept maps could further
engage both learners and instructors in an active learning approach and collaboration
setting (Shen et al., 2006; Kumar and Schwertner, 2008).
4. Conclusion
The paper described a large-scale knowledge-based project plan to improve teaching and
learning undertaken at the Malaysian Maritime Academy. The experience gained in smaller
pilot projects earlier; provide the impetus for this project. The project has the potential to
reduce the knowledge and proficiency gaps for the mariners of the future and thereby
improve operational safety and reduce human error. For successful implementation, the
project will need to get the support of industry stakeholders as well as technology support
from the academia.
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