Complex Task Ontology Conceptual Modelling: Towards the
Development of the Agriculture Operations Task Ontology
Elcio Abrahão
1
and André Riyuiti Hirakawa
2
1
Laboratory of Informatics, Robotics and Microeletronic of Montpellier (LIRMM),
CNRS & University of Montpellier, 161 Rue Ada, Montpellier, France
2
Department of Computer Engineering and Digital Systems, Polytechnic School,
University of São Paulo, Av. Prof. Luciano Gualberto, travessa 3, 158, São Paulo, Brazil
Keywords: AGROPTO, Task Ontology, Conceptual Modelling, OntoUML, Agriculture Operations, UFO.
Abstract: Different from domain ontologies, task ontologies must describe the knowledge from its structural and
behavioural views, considering aspects as sequence of execution, conditional deviation, external expected
and unexpected events interference, pre and post conditions, task granularity, agent participation,
geographic localization, resource consummation, production and change. Although the use of conceptual
models is well accepted to formally describe domain ontologies, there is little research about conceptual
models for complex task ontologies. This paper describes the ongoing research on the Agriculture
Operations Task Ontology (AGROPTO) where OntoUML is used to develop conceptual models to describe
complex task’s aspects and possible modelling solutions based on Unified Foundation Ontology (UFO). An
extension of the E-OntoUML, a language for modelling task ontologies, is suggested to describe methods
for modelling task objectives, external event interference, pre/post conditions and task execution state
modifications in order to guide future research.
1 INTRODUCTION
The development of knowledge-based systems is
directly related to knowledge acquisition,
representation, reuse and sharing. In the economy of
knowledge (Ducker, 1992) the information is the
key point to achieve competitive advantage and
develop software tools based on artificial
intelligence as deep learning and cognitive
computing. The data exponential growth of modern
domain applications reinforces the need of
information technology tools to: (1) identify and
acquire knowledge from different and heterogeneous
sources, (2) transform and interpret the data
intelligently, (3) provide a common shared formal
representation of data and associated knowledge. On
domains, like the agriculture field operations, the
knowledge-based systems must deal with the big
amount of data generated by precision agriculture
field sensors, crop cultivation and pre/post harvest
procedures. Data should be managed and shared
between all stakeholders on the supply chain
allowing well-founded strategy decision taken as
well as food traceability and wastage control.
Ontologies are commonly used to provide a
formalism to describe the data and well formed
semantics to define concepts associated to the body
of knowledge of a given domain as explained in the
next section.
1.1 Ontology
According to Gruber (1993), ontology is an explicit
specification of a conceptualization and from the
knowledge-based systems view, something that
“exists” is something that can be formally
represented. Guarino (1998) defines different kinds
of ontologies according to their level of generality:
(1) top-level ontologies, which describe very general
concepts such as time, space, objects, actions,
events, and are of common use in large domain
communities, (2) domain ontologies, which describe
the common vocabulary of a domain, (3) task
ontologies, which describe generic tasks or activities
by specializing terms of the top-level ontology and
(4) application ontologies, which describe concepts
from a particular domain, by specializing from both
domain and task ontologies. There are many works
presenting domain ontologies in several computer
science areas as domain engineering, artificial
Abrahão, E. and Hirakawa, A.
Complex Task Ontology Conceptual Modelling: Towards the Development of the Agriculture Operations Task Ontology.
DOI: 10.5220/0006956202870294
In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 2: KEOD, pages 287-294
ISBN: 978-989-758-330-8
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
287
intelligence and semantic web (Guizzardi, 2005).
There are also a variety of models proposed to
capture the concepts, relations and properties that
are relevant for the domain conceptualization
through a formal and structured conceptual model
(Martins and Falbo, 2008). An important
architecture decision to take while developing an
ontology is the selection of a foundation ontology as
discussed in the next section.
1.2 Foundation Ontologies
Foundation ontology is a high abstract
categorization to describe concepts that are
commonly used across different domains.
Foundation ontologies are systems of
philosophically well-founded categories and are
domain independent (Guizzardi, Falbo and
Guizzardi, 2008). Some foundation ontologies are
described in: (Lenat and Guha, 1990; Niles and
Pease, 2001; Guizzardi, 2005; Herre et al., 2006).
According to Guizzardi (2005), they already prove
to be important to increase the quality of modelling
languages and conceptual models. The same author
proposed a foundation ontology named UFO
(Unified Foundational Ontology). UFO is a
unification of GFO (Herre et al., 2006), OntoClean
(Guarino and Welty, 2009) and DOLCE (Bottazzi
and Ferrario, 2009) and it defines things, sets,
entities, individuals and types that are the
ontological foundation to build consistent and well-
formed conceptual models. According to (Guizzardi
et al., 2004), UFO is divided into three
complementary sets: (1) UFO-A, which defines the
core of UFO, (2) UFO-B, which defines the terms
related to Perdurants (i.e. type of individual) and (3)
UFO-C, which defines the terms related to the
spheres of intentional and social things, including
linguistic things. Guizzardi et al. (2009) also
proposed OntoUML, a conceptual modelling
language that contemplates, as modelling primitives,
the ontological distinctions proposed by the UFO-A
ontology. Automatized computational tools for
OntoUML, as described by (Guerson et al., 2015;
Moreira et al., 2016) helps in the development of
well founded conceptual models and they are used in
this research as described in the methodology
section.
1.3 Task Modelling
Knowledge becomes usable and useful only if it fits
the use-context. This is the justification for the
expert system technology that relies on heuristic
knowledge or on domain experts’ knowledge rather
than on objective knowledge like domain theory
(Ikeda et al., 1998). Still according to Mizoguchi,
Tijerino and Ikeda (1998), expert systems have high
performance at the cost of non-reusability of
knowledge and low productivity of the knowledge-
base development. One of the well-known ideas to
solve this problem is the decomposition of expertise
into two kinds of knowledge: (1) task-dependent
knowledge and (2) domain-dependent knowledge
(Mizoguchi, Tijerino and Ikeda, 1995). According to
Martins and Falbo (2008), a key factor to capture
knowledge is to have a model to represent it. As a
model is an abstract representation of a real system,
abstraction can be used to remove unnecessary or
irrelevant details from the system, so it can be better
understood (Gaffar et al., 2004). A model to
represent task-dependent knowledge should be able
to describe: (1) which tasks are necessary to perform
a goal, (2) the agents that perform the task, (3) the
task execution time interval and (4) which resources
(inputs) are consumed and which products (outputs)
are generated (Abrahão and Hirakawa, 2017). A
task ontology model can allow agents (humans or
machines) to infer knowledge about tasks using
semantic techniques for task recognition, negotiation
and relocation (Schmidt et al., 2015). Task
ontologies will be discussed in more details in the
following section.
1.4 Task Ontologies
According to Martins and Falbo (2008) and Martins
(2009), there are two major kinds of knowledge that
should be captured by a task ontology: (1) task
decomposition and flow-control, and (2) knowledge
roles played by objects from the domain in the task
fulfilment. The first kind represents the behaviour
view of the task and the second the structural view.
Both views need models that could represent the
behaviour and structure of a task. UML class and
activity diagrams are used to represent task ontology
models by (Martins and Falbo, 2008) and where
adapted by (Abrahão and Hirakawa, 2017) to
describe a task ontology for agriculture operations.
Martins (2009) proposed an extension for the
OntoUML to describe the knowledge of task
ontologies based on events called E-OntoUML. This
language uses UML class and activity diagrams to
represent task structural and behaviour views. E-
OntoUML captures: (1) structural knowledge
regarding the roles that domain entities will exert in
a task and its relationships, and (2) behavioural
knowledge, which defines the decomposition and
KEOD 2018 - 10th International Conference on Knowledge Engineering and Ontology Development
288
flow of control in the subtasks. Although the use of
conceptual models is well accepted to formally
describe domain ontologies, there is little research
about conceptual models for complex task
ontologies. As part of an ongoing research, the aim
of this paper is: (A) present the initial design of the
Agriculture Operations Task Ontology
(AGROPTO), a high level generic task ontology
independent of crop type; (B) Analyse task ontology
structural and behavioural aspects according to E-
OntoUML; (C) Propose possible solutions not
addressed by E-OntoUML as: (1) task objectives, (2)
external event interference, (3) pre and post
conditions, (D) Exemplify proposed solutions on an
use case for an agriculture field operation of
pesticide spraying and (E) Discuss next steps of
AGROPTO development and suggest a guide to
future work.
2 METHODOLOGY
This section describes the methodologies used to
develop the task ontology considering the structural
and behavioural aspects, the selection of a
foundational ontology, ontology reuse, conceptual
modelling and architecture design characteristics.
2.1 Ontology Development Methodology
Although the aim of this work is not to develop a
domain ontology for agriculture, it is necessary to
define the classes, attributes and relations that will
be part of the task ontology structure. It is important
to allow the reuse of other domain ontologies
(Bontas, Mochol and Tolksdorf, 2005) and the task
ontology architecture design should be domain
independent and, by itself, reusable by other task
and domain ontologies. There are many
methodologies recommended to develop domain
ontologies as described by (Uschold and King,
1995), (Gruninger, M., and Fox, 1995) and (Falbo,
2004). In this work, the methodology selected was
the one proposed by (Noy and Mcguinness, 2000),
as showed on Table 1.
2.2 Conceptual Modelling
Once a methodology to guide the development of
the task ontology was selected, a language to
describe the behavioural aspects of the tasks must
also be selected. There are some methodologies used
to represent the task’s behaviour as described in
(Bastos and Ruiz, 2002), (Prata, 2007), (Fersman,
Pettersson and Yi, 2002), (Mizoguchi, Tijerino and
Ikeda, 1995) and (Rajpathak, Hall and Keynes,
2001). In this work the E-OntoUML proposed by
(Martins and Falbo, 2008; Martins, 2009) was
selected. E-OntoUML is an extension of OntoUML
based on UFO and it provides a concise and robust
graphical representation of the structural and
behavioural aspects of tasks using, respectively,
UML class and activity diagrams. Furthermore, the
use of a foundation ontology adds more semantic to
task or domain ontologies, making them more
concise (Martins, 2009).
Table 1: Seven steps to create an ontology according to
(Noy and Mcguinness, 2000).
n# Description
1 Determine the domain and scope of the ontology
2 Consider reusing existing ontologies
3 Enumerate important terms in the ontology
4 Define the classes and the class hierarchy
5 Define the properties of classes—slots
6 Define the facets of the slots
7 Create instances
2.3 Software Tools
Although it is possible to draw OntoUML diagrams
with any regular UML software tool, this work
selected an OntoUML editor named Menthor
(Moreira et al., 2016) to benefit from the
automatized syntax verification and design pattern
validation of the tool. To produce the activity
diagrams from E-OntoUML, an open source UML
software named StarUML (StartUML, 2005) was
selected.
3 RESULTS AND DISCUSSION
The main class structures and relations of
AGROPTO are presented in this section. The partial
results are related to the steps 1 to 4 of the selected
development methodology (table 1) and are
explained in details in the following subsections.
3.1 AGROPTO Initial Design
The conceptual model for AGROPTO reuse the
patterns from E-OntoUML to describe the task
Complex Task Ontology Conceptual Modelling: Towards the Development of the Agriculture Operations Task Ontology
289
Figure 1: AGROPTO OntoUML model for basic task model structural aspects as task objectives, task composition and
decomposition, time relations, agent and resource participation and conditional deviation.
KEOD 2018 - 10th International Conference on Knowledge Engineering and Ontology Development
290
execution behaviour view. The E-OntoUML model
describes tasks, task aggregation, agents and objects
participation in tasks, time relations, task state
change and conditional derivation. The E-OntoUML
model components (Figure 1) are: (1) Worker:
physical agent that executes the task, (2) Activity: a
set of 2 or more Agent Actions. (3) Agent Action:
represents the task and is specialized in (4) Atomic
Agent Action: most granular task that could not be
divisible, (5) Complex Agent Action: a composition
of 2 or more Atomic Agent Actions and it is
specialized in: (6) Interaction: participation of 2 or
more Workers to perform a task and (7) Object
Agent Interaction: participation of a Worker and a
Resource in the task execution. Resources
participate in tasks through the relator (8) Resource
Participation Action, that specifies the Resource
Participation in 4 sub kinds: Resource Creation,
Change, Termination and Usage. (9) Resource: a
physical object subkind that is specialized in
Machinery (vehicle, supplement, tool), Place
(geographic localization) and Product (input: needed
to perform or used by task or output: generated as
result of task execution). Agent Action source and
target are framed by a (10) Time Interval specified
by time interval realtions derived from Allen’s time
relations (Allen, 1983). (11) Situation: is an instant
frame of the pre or post states of the task and it
satisfies a (12) Preposition Value Specification that
represents a conditional derivation on the UML
activity diagram. Other classes as (13) Company,
(14) Job, and (15) Service Order, where added to the
model to describe entities related to the agriculture
domain that participates in the structural view of the
task ontology. As E-OntoUML did not provide
representation for some complex task elements
present on agriculture field operations, this paper
presents possible solutions in the next subsection.
3.2 Complex Task’s Model
Figure 2 shows additional model elements proposed
by this work to describe (A) Task Objectives, (B)
Pre and Post Execution Conditions, (C) External
Event Interference and (D) Task Execution Status
Modifications. Task Objective is the goal of the task,
i.e. what the task should pursue as a final desirable
state. To represent the task objective a specialization
of the Situation class called (16) Objective Situation
was proposed. As Situation is a snapshot of a state in
a moment of time, the Objective Situation is a
snapshot of a desirable state where the task goal was
achieved. The (17) Condition class describes pre and
post execution conditions of the task. Every
Condition satisfies one or more (18) Proposition
Value Specification, which represents a statement
that imposes the Condition that will or not triggers
the task execution. One post condition for a task T
1
could be a pre condition for the task T
2..n
. Pre and
post conditions could then be applied to Agent
Action subclasses to an Activity. External event
interferences are represented in the model proposed
here by the (19) Trigger Event class. The Trigger
Event is specialized in two types: (20) Exception
Event: unexpected events that may cause an
operational interruption that will delay or interrupt
the accomplishment of the task objective and
happens on uncertain time moment and (21)
Ordinary Event: Expected events, but whose exact
time and place of occurrence are known only with
some degree of uncertainty. The Ordinary Event is
specialized in: (22) Planned Ordinary Event: this is
a planned and expected event and occurred at a
planned and regular time interval, and (23)
Unplanned Ordinary Event: Expected event, but
occurs at unplanned and irregular time intervals.
Trigger Event describes an event that will cause a
(24) Condition Modification. The Condition
Modification specifies a (25) Condition Action that
was specialized in (26) Condition Creation, (27)
Condition Change and (28) Condition Termination.
A Condition Modification, in turn, changes the
attributes of the Condition, and then a Preposition
could or could not be satisfied, triggering the
execution of a task or causing a modification in the
task execution state. The (29) Execution State Action
specifies what type of (30) Execution State was
modified by the Trigger Event: (31) Interruption,
(32) Cancelation or (33) Operational Delay. Both,
Trigger Event and Execution State Action happened
at a specific point in time (34) Time Point. Figure 3
shows the activity diagram for a use case of
pesticide spraying. The activity diagram represents
external events that interferes on the task execution
(1), pre-conditions for task execution (2) and
resource state changes due task execution (3). In this
use case the resource tractor can have two pre-
conditions to perform the task: be on mechanic
working condition and has an operator condition.
The resource land has one pre-condition:
appropriated soil moisture and the main task, by it
self, has one pre-condition too: appropriated wind
velocity (on the moment of spraying, to avoid
undesired derivation). The external events: mechanic
failure and change of working turn affects the
tractor resource. The land resource and main task
are affected by the weather external event. The
modifications on the task execution state, due the
Complex Task Ontology Conceptual Modelling: Towards the Development of the Agriculture Operations Task Ontology
291
Figure 2: AGROPTO OntoUML model for task pre/post conditions, external events interference and execution state
modifications.
external event interference, are represented on
Figure 4. This figure shows in detail the task state of
execution change when an weather event triggers the
pre-condition of wind velocity for the main task. The
activity node in (1) represents the execution state
action of the type interruption. Time points when the
event is trigged and the operation need to be
interrupted are represented by two instances of the
Time Point class (2). Although the E-OntoUML
addresses the basic elements of the a task ontology,
the proposed solutions presented here goes towards
the direction of complement complex task elements
descriptions considering well founded ontology
development guidelines. An implementation of
AGROPTO in the OWL (W3C, 2004) format is
available at http://agropto.org. Due to lack of space,
E-OntoUML more detailed activity diagrams for
AGROPTO where published on the ontology
website.
4 CONCLUSION
This work presented possible solutions to modelling
complex task ontologies not addressed by the E-
OnoUML as: task objectives, pre and post
conditions, external event interference and execution
state modification. The solutions proposed were
used on the Agriculture Operations Task Ontology, a
generic task ontology for the agriculture domain in
order to describe structural and behavioural aspects
of the task execution and generate a well-founded
implementation in OWL format. Future research
should continue the work on the next steps of the
methodology adopted to develop AGROPTO and a
more intensive analysis of the solutions proposed
here related to the concepts of UFO should be done.
Well-founded conceptual models for task ontologies
will contribute to create more concise and robust
knowledge representations on the ontology-
engineering domain.
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292
Figure 3: AGROPTO Pesticide Spraying E-OntoUML
activity diagram representing (1) external events
interference, (2) task pre-conditions and (3) resource state
change due task execution.
Figure 4: AGROPTO activity diagram to represent the
interruption of the execution state of the task (1), the point
in time when the external event was trigged and the task
effectively was interrupted (2).
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