Complex Task Ontology Conceptual Modelling: Towards the

Development of the Agriculture Operations Task Ontology

Elcio Abrahão

and André Riyuiti Hirakawa

Laboratory of Informatics, Robotics and Microeletronic of Montpellier (LIRMM),

CNRS & University of Montpellier, 161 Rue Ada, Montpellier, France

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

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

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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

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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

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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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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