Towards an Ontology of Task Dependence in Organizations

Mena Rizk

, Mark Fox

and Daniela Rosu

Enterprise Integration Laboratory, University of Toronto, 5 King’s College Road, Toronto, Canada

Keywords:

Coordination, Enterprise Modelling, Ontology, Organization Model, Task Dependence.

Abstract:

In the face of an increasingly dynamic, complex, and uncertain task environment, effective coordination is

crucial for organizational success. Based on a real-world case study investigating the nature of coordination

challenges in a municipal infrastructure project, we identiﬁed shortcomings in the representational frame-

works offered by organization studies and enterprise modelling that limit their ability to effectively model task

dependence and assist in improving coordination. Starting from existing organizational research literature

and domain expertise, we conducted an ontological analysis of task-related concepts, formulated represen-

tational requirements, and proposed a formalization. Our approach deﬁnes task dependence in terms of the

constraints one task imposes on another, underpinned by novel constructs that deﬁne how and why a task is

constrained. These constructs support the inference of dependencies between tasks, facilitating the discov-

ery of potentially hidden, latent dependencies. We formalized our conceptualization in an ontology, detailed

herein using ﬁrst-order logic. Consistency-veriﬁed implementations in Prover9 and OWL are provided. We

validated our approach by modelling and solving real-life scenarios provided by our domain-expert collabora-

tors. Our approach lays the groundwork for future extensions that will tackle the modelling of different forms

of dependence between agents within an organization.

1 INTRODUCTION

This paper introduces a novel model of task depen-

dence designed to assist organizations in overcom-

ing coordination challenges in complex, dynamic, and

unpredictable work environments.

The focus of this paper is on task dependencies —

the dependencies between tasks performed by agents,

rather than the agents themselves. While acknowl-

edging the existence of other forms of dependence

like epistemic, reward, and outcome (Puranam et al.,

2012; Raveendran et al., 2020), we intentionally ini-

tiate our endeavour at the task level with the aim to

robustly represent the inherent circumstances that ne-

cessitate coordination, and the tangible consequences

of dependencies on task execution. This allows for the

modelling of the irreducible dependencies that pertain

to the nature of work undertaken by an organization,

and the subsequent implications on the integration of

work, irrespective of agents’ work assignment.

We grounded our efforts in a case study (Section

2) conducted with one of North America’s ten most

https://orcid.org/0009-0008-6095-0100

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https://orcid.org/0000-0002-5877-9681

populous cities, identifying coordination challenges

within a large-scale infrastructure project, and ana-

lyzing the representational requirements for reason-

ing about their resolution. In Section 3.1, we present

an ontological analysis of task, task structure, and

task dependence concepts and introduce deﬁnitions

for each concept that are informed by both the ex-

isting organizational literature and the use cases pro-

vided by domain experts. A review of the relevant

literature (provided in Section 3.2) discusses the rep-

resentational limitations of the existing frameworks.

Subsequently, we present our working conceptualiza-

tion of task and task dependence, in Section 4. Its

formalization in ﬁrst-order logic is shown in Section

5. We validated our approach’s capacity to represent

dependencies and demonstrated its reasoning affor-

dances by applying it to scenarios from a real-life case

study (Section 6). We discuss its advantages and lim-

itations in Section 7 and conclude with our future re-

search direction in Section 8.

Rizk, M., Fox, M. and Rosu, D.

Towards an Ontology of Task Dependence in Organizations.

DOI: 10.5220/0012238300003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 2: KEOD, pages 96-107

ISBN: 978-989-758-671-2; ISSN: 2184-3228

2 MOTIVATION

2.1 Case Study

The research discussed herein is based on a partner-

ship with the municipal government of one of the

ten largest cities in North America (henceforth re-

ferred to as “the City”). The objective of this on-

going partnership is to explore the potential for in-

formation technologies in aiding the City’s coordi-

nation of complex infrastructure projects. With the

City-led infrastructure projects growing larger, faster,

and more complex, effective coordination across the

City’s divisions, stakeholders, projects, and resources

has become critical for successful project delivery. A

case study of a major current infrastructure project

provided initial insights into the current coordination

practices and identiﬁed areas of potential improve-

ment using information technology.

The undertaking discussed in this section is con-

cerned with the City’s ﬂood risk mitigation efforts

within one of its most ﬂood-prone areas. The Riverine

Flooding Project (RFP), aimed at reducing riverine

ﬂooding risks in the neighbourhood, involves design-

ing and implementing several pieces of infrastructure

such as bridges and concrete channels. Concurrent

with this, the City’s Urban Flooding Program (UFP)

is implementing sewage infrastructure improvements

that must be coordinated with the RFP. There are also

several planned capital works projects in the area that

the RFP must consider in its design. As operating in

this complex environment entails intense coordination

across various organizations, stakeholders, and tasks,

we had access to a wealth of coordination phenomena

and challenges to model and help address.

2.2 Challenges

While a detailed report on the case study will be

released separately, we outline our ﬁndings on the

nature of the coordination challenges for the pur-

pose of this paper. We identiﬁed three main types

of coordination challenges: participation, navigation,

and cross-cutting collaboration. Participation relates

to delays caused by inadequate stakeholder respon-

siveness. Navigation involves difﬁculties in identi-

fying affected stakeholders and accounting for their

needs. Cross-cutting collaboration is about synchro-

nizing the efforts of numerous stakeholders across

bureaucratic silos to meet interdependent objectives,

as opposed to independent pursuits that overlook the

broader project context.

Collectively, these challenges present consider-

able risks to the quality, budget, and timeline of in-

frastructure projects. Indeed, we identiﬁed cases of

delays, cost overruns, and suboptimal implementa-

tions that required rebuilding due to failures in partic-

ipation, navigation, and cross-cutting collaboration.

To successfully respond to these coordination

challenges, it is essential to identify the dependencies

necessitating coordination, the causes of coordination

failures, and strategies to mitigate the risk of future

failures. Therefore, an effective AI-enabled interven-

tion should be grounded in a robust representation of

dependence and the entities through which dependen-

cies materialize. In particular, we require the ability to

automatically infer instances of dependence, and cap-

ture the underlying causes and effects of dependen-

cies. As we will show in Section 3, the existing def-

initions of dependence and approaches to modelling

tasks are insufﬁcient for addressing the observed co-

ordination challenges. We tackled these shortcomings

by making explicit the representational requirements

for capturing tasks and task dependencies and devel-

oping a new approach to modelling them, which we

introduce in the next section.

3 LITERATURE REVIEW

3.1 Ontological Analysis of Task

Dependence

This section describes the ontological analysis we

performed in order to provide a solid foundation for

the design and development of our framework. We

examined the organizational literature in pursuit of

understanding the underlying semantics of the con-

cepts of interest, and their relationships, as well as un-

covering any implicit assumptions or inconsistencies

that may be present. We established representational

requirements for our framework based on the previ-

ously outlined challenges and then generated a set

of dimensions that are relevant for effectively deﬁn-

ing task dependence. These dimensions were sub-

sequently used to evaluate existing deﬁnitions and

implied conceptualizations in the existing literature,

and then propose our own deﬁnitions. With these in

hand, we reviewed the literature relevant to task mod-

elling (Section 3.2) in order to ﬁnd frameworks that

could help operationalize our deﬁnitions and shape

our model.

3.1.1 Dimensions of Task Dependence

As touched upon in Section 2, a representation of the

dependencies that call for coordination is a prerequi-

site for any reasoning about the observed coordination

Towards an Ontology of Task Dependence in Organizations

challenges. Speciﬁcally, we need a model that repre-

sents the dependent entities (the subjects in need of

coordination), the situations that make them depen-

dent (the necessity for coordination), and the implica-

tions that the dependency has for them (the function

of coordination). From these reasoning tasks, a dis-

tinct set of representational requirements becomes ap-

parent for any model that we wish to utilize to aid our

use cases. More precisely, we need a model that cap-

tures the organization’s work, the dependency rela-

tionship’s basis between work, and the dependency’s

effects on the performance of work. Furthermore, we

need to capture each of these three requirements at

various levels of abstraction in the task structure, such

as the goal level and detailed activity level.

Our model must include a representation of the ac-

tivities to be performed and the desired states to be

achieved (i.e., goals). Therefore, our model must dis-

tinguish between states, the desire to achieve states,

the intention to work towards desired states, and the

speciﬁc activities performed to work towards desired

states. The “structure” of work, often referred to as

the task structure, must also be represented, which es-

sentially describes the decomposition of work.

To infer and model dependencies, we need to cap-

ture the fundamental “basis” of dependence between

two tasks. This can be seen as the core reason one task

may depend on another, or the irreducible essence of a

dependency relationship between two tasks (Herbsleb

and Roberts, 2006). To comprehend why a coordina-

tion failure may have occurred or to develop strategies

and mechanisms to mitigate them, we need to capture

the “effect” of a task dependency on the dependent

task. This effect indicates how a dependency inﬂu-

ences task execution.

Moreover, to reason about task dependencies in

scenarios where the dependency’s exact nature cannot

be detailed, due to it being unknown or changing too

rapidly to warrant a speciﬁcation, we need the deﬁni-

tion of dependence to consider how it can be applied

to capture dependencies at various abstraction levels

of the work being analyzed. This highlights the dual

usage of the term “task” in the literature (achieve a

goal versus perform an activity), which we aim to dis-

ambiguate by this section’s end. For now, we refer to

this requirement as the “context” of a dependency.

Therefore, to meet the needs of our representa-

tional requirements, any approach for modelling task

dependence must explicitly (1) distinguish between

agent and task dependence; and allow for the (2) “ba-

sis”, (3) “effect” and (4) “context” of task dependence

to be made explicit. We will refer to these four condi-

tions in Section 3.1.5.

The underlying deﬁnitions for task and task struc-

ture must also be accounted for and must be con-

sistent, as these are the entities between which de-

pendencies exist, and the task structure serves as the

medium for these dependencies.

3.1.2 Deﬁnitions from Organizational Research

Multiple attempts to deﬁne task dependence exist in

the organizational literature. We focus our analysis

on four deﬁnitions due to their distinct perspectives,

allowing us to examine different elements of their def-

initions regarding the four dimensions we seek to ana-

lyze. For a more comprehensive review of task depen-

dence, we refer readers to (Raveendran et al., 2020).

Thompson (1967) provides one of the earliest def-

initions of task dependence, grounded in the relation-

ship between tasks’ inputs and outputs. He proposed

three types of possible task dependencies: pooled

(both tasks contribute to the same output), sequen-

tial (one task’s output is the input to another), and

reciprocal (both tasks feed into each other). Crow-

ston (1994), drawing from distributed artiﬁcial intel-

ligence literature, asserts that two tasks can only be

dependent via a common resource (which includes

states). He speciﬁes three possible types of depen-

dencies: overlapping effects (two tasks contribute to

producing, or causing if the resource is a state, the

same resource); overlapping preconditions (two tasks

require the same resource); and overlapping effects

and preconditions (the resource produced by one task

is required by another). More recently, coming from

the emerging microstructural perspective, Puranam

(2018) suggests task interdependence is present when

the value generated from performing each task is dif-

ferent, depending on whether the other task is per-

formed or not. Finally, Raveendran et al. (2020) con-

sider task dependence’s role in informing organiza-

tion designs, depending on the nature of work being

well or ill-understood, further highlighting the context

of task dependence.

3.1.3 Analysis of Task

Prior to analyzing the four approaches’ interpretation

of task dependence, we need to grasp their fundamen-

tal assumptions about the nature of tasks and their

structure.

Thompson, while not explicitly deﬁning a task,

implies that a task is a speciﬁc function or activity em-

bedded within a workﬂow, undertaken by agents with

deﬁned inputs and outputs. The concept of “intention-

ality”, or the goal achieved by a task, is absent from

this deﬁnition. While intentionality may be argued to

be implicitly deﬁned through the workﬂow in which

a given task is embedded, it may become increasingly

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

obscured as the task deﬁned becomes more granular.

Crowston expands on this by considering tasks to

encompass both goal achievement and activity perfor-

mance. He characterizes goals as desired states and

activities as actions taken to achieve particular states.

However, Crowston’s harmonization of “achieving

goals” and “performing activities” under the single

concept of “task” is problematic. Goals and activ-

ities are inherently different; a goal is a desire to-

wards a state, while an activity is an action causing

a state. Moreover, Crowston’s confounding of re-

sources with states, and outcomes with outputs, fur-

ther limits the operationalization of this approach for

deﬁning task dependence. To illustrate the limitations

of this approach, we can reference an upper-level on-

tology, such as DOLCE (Borgo et al., 2022). Re-

sources and outputs (resources that are produced by

an activity) are endurants while states and outcomes

(states caused by an activity) are perdurants.

Puranam provides a more explicit deﬁnition, de-

scribing a task as a transformation of inputs into

outputs in ﬁnite time, with an associated value de-

rived from the difference between inputs and outputs.

However, the relationship to a task’s corresponding

goals is not deﬁned. Finally, Raveendran et al. fol-

low Puranam’s deﬁnition but separately consider goal

dependence. They focus on dependencies between

agents sharing common goals rather than dependen-

cies between goals themselves. This approach leaves

gaps in capturing task dependencies when the partic-

ular transformations for achieving these goals are not

clearly deﬁned.

3.1.4 Analysis of Task Structure

In Thompson’s approach, the conceptualization of

task structure does not extend beyond the notion of

“workﬂow” and the categorization of activities into

input, technological, and output tasks.

Crowston’s view of task structure can be described

as a “means-ends” decomposition of intended “ef-

fects” (goals) into possible subgoals and primitive

activities. This approach attempts to streamline the

analysis of tasks by harmonizing goals and activities.

However, the confounding of states, intentions, and

actions confuses the deﬁnition, preventing a clear and

unambiguous formal representation of task structure.

Puranam deﬁnes a task structure as the most ﬁne-

grained means-end decomposition of an organiza-

tion’s goals into its constituent tasks and their inter-

dependencies, operationalized using design structure

matrices (Baldwin and Clark, 2000). However, this

approach raises a couple of issues. Firstly, the ab-

sence of explicit representation of goals, intermedi-

ate goals, and their relationship to activities is prob-

lematic. Unlike Crowston, who incorporated the de-

composition of goals into actions in his notion of task

structure, Puranam’s approach lacks this dimension

explicitly. Instead, decomposition is implicitly de-

ﬁned through the clustering of interdependent tasks.

This absence hinders us from modelling the connec-

tion between a given goal and the actions required

to realize it, largely limiting our ability to precisely

represent the decomposition of high-level tasks into

their constituent sub-tasks and corresponding sub-

states they are intended to achieve. Secondly, the

modelling of decomposition is paradoxically bottom-

up rather than top-down. Task structure is deﬁned

based on task dependencies, rather than deﬁning task

dependence based on task structure. This can be lim-

iting when our goal is to infer dependencies between

tasks that are hard to detect, given a representation of

the tasks.

The limitations associated with Puranam’s ap-

proach to modelling task structure are also applicable

to Raveendran et al.’s approach. They suggest that the

level of understanding of a work environment can be

expressed in terms of the extent to which the underly-

ing task structure can be modelled in terms of detailed

tasks. However, this approach does not solve the is-

sues raised in Puranam’s, maintaining the difﬁculties

for our purpose of inferring obscure dependencies.

3.1.5 Analysis of Task Dependence

In this section, we explore how each of the four

task dependence approaches meets the four condi-

tions speciﬁed at the end of Section 3.1.1.

Separation from Agent Dependence. Thompson

deﬁnes dependence between workﬂows across orga-

nizational units, implying task dependence is agent

dependence caused by the relationship between the

tasks of the dependent agents. Conversely, Crowston

and Puranam assert task dependence as separate from

agents. Furthermore, Puranam shows that task depen-

dence is neither a necessary nor sufﬁcient condition

for agents of interdependent tasks to be interdepen-

dent, challenging Thompson’s perspective. Instead,

the relationship between task and agent dependence is

inﬂuenced by the extent to which the tasks are under-

stood, as noted by Raveendran et al. (2020). In well-

understood environments, agent dependencies stem

from task dependence, through task allocation deci-

sions. In less clear environments, task dependence

emerges from the collective sense-making processes

shaped by agent dependence.

Basis of Task Dependence. Thompson views task

dependence as arising from workﬂow directionality,

which is deﬁned in terms of overlaps between the in-

puts and outputs of tasks. In Crowston’s approach, the

Towards an Ontology of Task Dependence in Organizations

basis of dependence is the common resource through

which two tasks have an overlap in their precondi-

tions and/or effects. Both approaches have limita-

tions. Thompson’s approach overlooks dependencies

based on information ﬂows that are not necessarily

isomorphic with workﬂow directionality. Crowston’s

approach, however, captures some of these dependen-

cies, though not without its own limitations, as will be

shown shortly. For instance, Malone and Crowston

(1994), show that a producer-consumer relationship

between two tasks (i.e., the output of one task is the

input of another) may be due to either an inventory

(the second task can only execute after the output of

the ﬁrst is available) or usability constraint (the ﬁrst

task must produce its output in a way that is usable

by the second task). The former constraint, a sequen-

tial dependency in Thompson’s language, matches the

workﬂow direction. However, the latter constraint,

grounded in information ﬂow, runs in the opposite di-

rection to workﬂow and would not be deﬁnable with

Thompson’s approach.

While broader than Thompson’s basis, a limita-

tion of Crowston’s approach is that it is grounded in

whether a common resource exists between two tasks,

without an explicit representation of the underlying

reason why the common resource forms a depen-

dency. For instance, the distinction between inven-

tory and usability constraints is left to a human rea-

soner applying the model. This distinction is impor-

tant since the reason why a common resource causes a

dependency has implications for how the dependency

ought to be managed, as we’ll see shortly when dis-

cussing “effects”.

Puranam and Raveendran et al. propose a differ-

ent perspective, focusing on the mutual inﬂuence be-

tween tasks on the value they generate as the basis of

dependence. However, the basis here is overly con-

strained as it only considers if the value of one task

varies with if another task is performed, rather than

how it is performed. Additionally, though “value” is

intended to be an abstract construct which can be arbi-

trarily deﬁned, the approach does not allow for an ex-

plicit representation of why a differential in the value

of a task exists in the ﬁrst place.

Effect of Task Dependence. Thompson’s ap-

proach to managing task dependencies involves var-

ious coordination mechanisms that vary in “strength”

based on the type of task dependence. This suggests

effects of task dependencies include interaction inten-

sity and complexity. However, Thompson’s frame-

work does not capture the precise ways in which a

task can be constrained by dependencies.

Crowston and Puranam also touch on “effects”

but don’t explicitly address how tasks may vary in

their performance due to dependencies. For each type

of dependency, Malone and Crowston (1994) offer a

set of coordination mechanisms for managing the de-

pendency, but they do not explicitly model the pre-

cise way in which the coordination mechanisms af-

fect how tasks are performed (i.e., why the coordina-

tion mechanisms work). For Puranam’s approach, the

speciﬁc dimensions of a task’s performance that may

change due to its value relation to another task are

not explicitly deﬁned. Without a representation of the

“effects” of dependence on the performance of a task,

we are limited in our ability to reason about the efﬁ-

cacy of alternative coordination strategies for a given

dependency.

Context of Task Dependence. Given that the

approaches offered by Thompson, Puranam, and

Raveendran et al. view tasks as well-deﬁned opera-

tions, they are limited in their ability to capture task

dependencies in environments where the nature of

work is not well understood and tasks cannot be spec-

iﬁed beyond the goals they are intended to achieve

(i.e., the speciﬁc activities are either unknown or can-

not be explicitly represented). Crowston’s deﬁnition

of tasks as both the achievement of goals and the

performance of activities allows for the capturing of

dependencies in ill-understood work environments,

where only dependencies between the achievement

of goals can be represented. However, due to the

limitations speciﬁed in Section 3.1.3, operationaliz-

ing Crowston’s approach is a challenge.

3.1.6 Proposed Deﬁnitions

Based on the ontological analysis of tasks, task struc-

ture, and task dependence, we have demonstrated that

the approaches offered by the organizational literature

are neither consistent with one another, nor do they

adequately satisfy the conditions that we require to

reason about addressing the coordination challenges

we found in practice. Below, based on our ontologi-

cal analysis and required conditions, we propose a set

of deﬁnitions that form the basis of our model:

• Task: The intention of an agent(s) to work to-

wards some goal. A well-understood task can be

further speciﬁed in terms of activities that when

executed cause the desired states, whereas an ill-

understood task can only be speciﬁed in terms of

the goal that is intended to be accomplished.

• Activity: An activity can be performed by an

agent, and may have inputs (required resources)

and outputs (produced resources). An activity

causes outcomes, which are the consequences of

an activity’s performance. Activities may have

characteristics which deﬁne particular ways in

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

100

which the performance of the activity can vary.

• Task Structure: A task structure is a decompo-

sition of tasks into the constituent activities that

must be performed, and the corresponding states

that must hold in order for the goal of the task

to be achieved. Activities that cause states may

be decomposed into subactivities that cause sub-

states, and so forth. In essence, a task structure

speciﬁes “how” a task to bring about some goal

is achieved in terms of the activities that must be

performed and the states that must be true.

• Task and Activity Dependence: Task A is de-

pendent on Task B if the way in which the goal of

A is achieved is constrained by the way in which

the goal of B is achieved. Activity A is dependent

on Activity B if the way in which A is performed

is constrained by the performance of B.

Here, we explicitly capture that the basis of the

dependency is that there are constraints placed on the

performance of activity A by the performance of B.

Additionally, the concept of “value” is implicitly cap-

tured since it can be assumed that the basis of con-

straint is that in order for the activity to be performed

in a way that maximizes its value, the particular way

in which it should (or must) be performed may vary

with how another activity is performed (rather than

just if ). More so, the distinction between task and ac-

tivity dependence allows us to capture dependencies

between work in both well- and ill-understood envi-

ronments.

3.2 Related Task Frameworks

The concept of a task has been extensively examined

across various domains, ranging from psychology

(Annett and Duncan, 1967) to organizational stud-

ies and information and computer science (Vernadat,

2020; Alam et al., 2015; Guizzardi et al., 2013; Yu

and Mylopoulos, 1995; Greenspan et al., 1994), pre-

senting an array of perspectives and methodologies.

These span from cognitive theory-based methods fo-

cusing on human thought and behaviour, to methods

designed to endow robots with autonomy.

An early, and subsequently prominent task mod-

elling framework, the Hierarchical Task Analysis

(HTA) (Annett and Duncan, 1967) supported the view

that a task is “any piece of work that has to be done”

and that task performance “is a goal-directed be-

haviour”. Their approach, departing from earlier ap-

proaches, was to offer functional analysis rather than

behavioural descriptions of tasks, initiating the anal-

ysis from the task’s goals rather than associated ac-

tivities. However, despite its name, HTA, does not,

in fact, emphasize tasks, but focuses instead on goals

and the operations to achieve them. Due to its onto-

logical commitments, this framework, and its subse-

quent extensions, do not provide the means to capture

and distinguish an activity from the intent to perform

it, which is an essential requirement that our represen-

tational framework strives to meet.

Successive approaches (Phipps et al., 2011; Stan-

ton, 2006) emerged in organizational sciences as well

as computer and information science. Of particular

relevance to our work are the frameworks developed

from the ﬁeld of enterprise modelling, which sits at

the intersection of IT, information science, and orga-

nization studies. Also relevant, from the computer

science literature, are frameworks emerging from the

ﬁelds of requirements engineering (e.g., i*, KAOS)

and AI planning.

Enterprise modelling, deﬁned by Fox and

uninger (1998) as “a computational representation

of the structure, activities, processes, information re-

sources, people, behaviour, goals and constraints of a

business, government, or other enterprise”, has gen-

erated various complementary frameworks over four

decades. These are centred around activities (e.g.,

IDEF, MERISE), business processes (e.g., ARIS,

CIMOSA), and enterprise knowledge (e.g., DEMO

by Dietz,1999; and TOVE by Fox et al.,1995). While

these frameworks incorporate elements of agents,

goals, tasks, and activities, none distinguish between

an activity and the intent to execute it.

AI planning shares similar concerns with our cur-

rent work, striving for computationally executable

plans along with methodologies for social and cog-

nitive plans. Existing methodologies (e.g., Chan-

drasekaran and Josephson, 1997; and Bermejo-

Alonso, 2018) do attempt to capture the activity to

perform and how it should be executed as part of a

plan. However, they do not account for the intent to

perform an activity, a crucial distinction from the goal

the activity is designed or expected to achieve.

There are also domain-independent efforts, such

as upper-level ontology work (e.g., BFO

, UFO

DOLCE

, DOLCE-Lite-Plus

) that offer a mecha-

nism for modelling agents, goals, tasks, and actions.

DOLCE-Lite-Plus, in particular, deﬁnes a task as a

“course used to sequence activities or other control-

lable perdurants”, where a course is a “concept that

selects (in particular, it sequences) perdurants (pro-

cesses, events, or states), as a component of some s-

https://basic-formal-ontology.org/

https://nemo.inf.ufes.br/en/projetos/ufo/

http://www.loa.istc.cnr.it/dolce/overview.html

https://www.w3.org/2001/sw/BestPractices/WNET/

DLP3941 daml.html

Towards an Ontology of Task Dependence in Organizations

101

description” (which relates, roles, endurants and con-

texts). In effect, tasks are (the desired) targets of some

role played by an agent and can relate to ground ac-

tivities or decision-making. In this framework, tasks

are explicitly declared to be disjoint from actions and

action types, an ontological stance that is compatible

with our representational requirements.

4 CONCEPTUAL MODEL

In this section, we introduce our conceptual model,

with each subsection covering a different layer. Fig-

ure 1 displays the ontology design pattern, highlight-

ing the main concepts and relations discussed.

4.1 Agent, Intentions, and Desires

The foundational concepts in our model are agents,

their tasks, and their goals. We deﬁne an Agent as

an entity capable of possessing goals, having inten-

tions to strive for those goals, and executing actions

to attain them. Agents can be individuals, teams, de-

partments, or entire organizations and even temporar-

ily formed entities like committees or task forces. A

Task signiﬁes an agent’s intention to exert effort to-

wards a goal. It is deﬁned in terms of the agent with

the intent and the goal, which is the target of this in-

tent. A Goal represents a desired “state of affairs”.

It is deﬁned in terms of the agent who has the goal

and the desired State (detailed later) of the agent. At

this stage, we don’t consider the reasons behind an

agent’s goal or task. Also worth noting, our model

doesn’t incorporate organizational roles since our fo-

cus is on capturing dependencies between work, not

dependent on speciﬁc organizational settings or for-

mal structures.

Tasks may also be deﬁned via Activities through

which goals can be achieved. The difference between

tasks and activities is important yet subtle. Tasks con-

vey the intent to work towards a goal, while activities

are concrete operations that might lead to a state, re-

gardless of whether that state is desirable. Tasks rep-

resent the intention to act towards a goal, while activ-

ities specify the exact manner in which one can act.

This distinction is useful in contrasting well-

deﬁned and ill-deﬁned task structures. For well-

deﬁned task structures, where necessary actions to

achieve a goal are known, we can understand the spe-

ciﬁc contribution of each agent. Conversely, in ill-

deﬁned task structures, where agents’ speciﬁc actions

are unpredictable or can’t be predeﬁned, we can still

capture their collective intention towards the shared

goal. Furthermore, this distinction allows us to ex-

press task dependence in terms of achieving goals

rather than merely performing activities. In scenar-

ios where the accomplishment of one goal inﬂuences

or restricts another, but the exact method of inﬂuence

can’t be predetermined (as in ill-deﬁned task struc-

tures), we model tasks as the intention to work to-

wards a goal. This way, we can articulate the inﬂu-

ence of one goal on another through the tasks involved

in achieving both goals. Thus, the achievement of

a state may be constrained by how another state is

achieved, even if the precise “how” (i.e., an activity)

is not known or cannot be represented. This gives us

a higher level of representation, at the level of “inten-

tion to act towards” rather than the speciﬁc “act”.

4.2 Activities and Resources

An Activity refers to a well-deﬁned operation that an

agent can perform as part of a task, causing an out-

come state. It may also be enabled by a state and may

require or produce a Resource. An activity can be

seen as a production technology (Puranam, 2018) or

an IDEF

0 function. An activity may have an Activ-

ity Decomposition, which breaks down complex ac-

tivities into constituent sub-activities. This construct

captures relationships between activities at different

abstraction levels, reminiscent of hierarchical mod-

elling in IDEF

0 or the AggregateActivity construct in

TOVE (Fox et al., 1993). Explicit modelling of the

decomposition as a construct allows representation,

querying, and visualization of task structures at var-

ious abstraction levels. Capturing the speciﬁc ways

in which an activity’s performance can vary is crucial

for activity dependence (the performance of one ac-

tivity being inﬂuenced by another). For this, we use

the Activity Characteristic construct, deﬁning a feature

of an activity subject to variation. Not every activity

characteristic may be part of a dependency. We clas-

sify these characteristics into ﬁve main categories (not

intended to be collectively exhaustive):

• Input characteristics: Variations in activity inputs

such as material selection, resource consumption,

cost, and information source.

• Process characteristics: Variations in the method

or implementation of an activity.

• Output characteristics: Activity performance di-

mensions that deﬁne the output, like quality,

quantity, and design.

• Spatial characteristics: Activity performance di-

mensions related to the physical or virtual loca-

tion where an activity occurs.

• Temporal characteristics: Temporal features such

as start time, end time, and duration.

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

102

Figure 1: Task dependence ontology design pattern showing main classes and properties (dashed arrows for subclassOf).

4.3 States

A State describes a particular aspect of an object or

situation, capturing the idea that the modelled object

or situation can have varying conditions over time.

States can be complex or atomic. A Complex State

can be either a Conjunctive State or a Disjunctive

State, representing the conjunction or disjunction of

other (sub)states, respectively.

An Atomic State is deﬁned in terms of a state

characteristic, operator, and value combination. The

State Characteristic is an identiﬁable property of an

object or situation. The Operator deﬁnes the relation-

ship between a characteristic and value (e.g. ≤, ≥,

=, ̸=). The Value represents the speciﬁc value of the

state characteristic deﬁned in an atomic state. It can

be based on a unit of measure, an ordinal or nom-

inal scale, or any data type. This ﬂexibility allows

for the representation of speciﬁc states and reason-

ing about the achievement of goals that can be sat-

isﬁed as well as satisﬁced (Simon, 1947), a feature

also seen in i* (Yu and Mylopoulos, 1995). This is

useful when the exact condition for state satisfaction

cannot be predetermined (e.g., minimize construction

costs) or when a particular state characteristic is hard

to quantify (e.g., improve design aesthetic).

4.4 Dependence

A Basis of Dependence captures the underlying rea-

son for one task, activity, or activity characteristic de-

pending on another, establishing the nature of their

relationship. This construct is key to our model, as

it underpins how task and activity dependencies are

inferred. A basis of dependence may constrain or be

altered by how a task is carried out, an activity is per-

formed, or an activity characteristic varies. If a task or

activity (or one of its characteristics) is constrained by

a basis of dependence and that basis is altered by an-

other task or activity, then the former entity depends

on the latter. The semantics of the depends on rela-

tion vary based on the class of entities being related.

Also crucial to deﬁning a basis of dependence is

its dependum – the entity through which a basis of de-

pendence exists. For example, if an activity produces

a resource used by another, then the availability of the

resource is the basis of dependence and the resource

is the dependum. While this is not an exhaustive clas-

siﬁcation of all possible bases of dependence, we’ve

identiﬁed six that collectively allow the representation

of a variety of dependencies between organizational

tasks and activities:

• Availability: The availability of a common re-

source (either as a common input or intermedi-

ary object) may be the basis of dependence be-

tween two tasks or activities. Here, the common

resource is the dependum.

• Functional: Arises when the ability to perform

an activity (both dependum and subject of depen-

dence) varies with if or how another activity is

performed or task accomplished.

• Complementarity: This basis of dependence

comes into play when the overall effects of two

activities or tasks are either greater or less than

the sum of their parts. The non-additive state char-

acteristic is the dependum here. For example, to

minimize trafﬁc disruptions, a municipality may

seek to bundle several planned maintenance activ-

ities (e.g.; road repavement, sewer improvements,

utility relocation) to occur at the same time.

Towards an Ontology of Task Dependence in Organizations

103

• Compatibility: This basis of dependence arises

when two tasks or activities need to coexist, due

to some state or activity characteristic condition,

but may not functionally inﬂuence each other.

• Uncertainty: This basis of dependence comes into

play when two tasks or activities depend on each

other due to the uncertainty of a state or activity

characteristic.

• Complexity: This basis of dependence applies

when the exact nature of the dependency relation-

ship between two tasks or activities is unknown or

cannot be explicitly stated.

We don’t impose any set relations between these

bases of dependence; depending on the domain the

ontology is applied to, some bases may be sub-bases

of others or disjoint. The complexity basis may serve

as a catch-all for bases of dependence that can’t be

captured by the ones we’ve deﬁned.

To illustrate the model, consider two activities

requiring the same non-shareable, non-consumable,

unary-capacity resource. An availability basis of de-

pendence exists between them through the shared re-

source. Since the start time of either activity affects

the resource’s availability, both activity characteris-

tics “alter” the basis of dependence. As the resource

availability constrains when each activity can start,

the basis of dependence “constrains” both activities.

Hence, they both depend on each other.

5 FORMALIZATION AND

IMPLEMENTATION

In this section, we provide a brief snapshot of

our ontology-based formalization of the conceptual

model, formulated in ﬁrst-order logic (FOL). Cho-

sen for its expressivity and the ability to capture ad-

vanced rules, FOL equips us with the complex rea-

soning capabilities necessary for our use cases, such

as inferring instances of dependence. Despite its po-

tential lack of decidability that might make alterna-

tives like description logic appealing, we opted to ﬁrst

construct our model “as intended” before translating

into a description logic, avoiding upfront constraints

on expressivity and inference.

Space constraints necessitate us to only include in

this paper the axiomatization of a subset of the depen-

dency inferences between tasks, activities, and activ-

ity characteristics. Other constructs underpinning ax-

ioms (1) to (5) are summarized in Table 1. The current

version of the full axiomatization can be found in the

Task Dependence Ontology Github repository

. The

FOL speciﬁcation was implemented in Prover9 and

veriﬁed for consistency. Additionally, we developed

a consistency-veriﬁed OWL (and SWRL) implemen-

tation to allow for further testing and future integra-

tion of the ontology into decision-support tools. Both

implementations can be found in the repository.

An activity characteristic dependsOn another ac-

tivity characteristic if there is a basis of dependence

that constrains the former and is also altered by the

latter:

∀b, a

, a

, c

(constrains(b, c

)

∧alteredBy(b, c

) ∧ (c

̸= c

)

∧hasActivityCharacteristic(a

, c

)

∧hasActivityCharacteristic(a

, c

)

→ dependsOn(c

, c

))

(1)

If an activity characteristic dependsOn another activ-

ity characteristic, then the activity of the former char-

acteristic is dependent on the activity of the second

characteristic:

∀a

, a

, c

(Activity(a

) ∧ Activity(a

)

∧hasActivityCharacteristic(a

, c

)

∧hasActivityCharacteristic(a

, c

)

∧dependsOn(c

, c

) ∧ (a

̸= a

)

→ dependsOn(a

, a

))

(2)

If an activity dependsOn another activity, then the

task that the former activity is a part of is dependent

on the task that the second activity is a part of:

∀t

, t

, a

(Task(t

) ∧ Task(t

)

∧hasActivity(t

, a

) ∧ hasActivity(t

, a

)

∧dependsOn(a

, a

) ∧ (t

̸= t

)

→ dependsOn(t

, t

))

(3)

An activity dependsOn another activity if there is a

basis of dependence that constrains the former and is

also altered by the latter:

∀a

, a

, b(Activity(a

) ∧ Activity(a

)

∧constrains(b, a

) ∧ alteredBy(b, a

)

∧(a

̸= a

) → dependsOn(a

, a

))

(4)

A task dependsOn another task if there is a basis of

dependence that constrains the former and is also al-

tered by the latter:

∀t

, t

, b(Task(t

) ∧ Task(t

) ∧ constrains(b, t

)

∧alteredBy(b, t

) ∧ (t

̸= t

) → dependsOn(t

, t

))

(5)

6 APPLICATION

In this section, we describe our framework’s appli-

cation to the project introduced in Section 2 to val-

idate its representational effectiveness (visualized in

https://github.com/rizkmena/Task-Dependence-Ontology

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

104

Table 1: Summary of axiomatization of the task dependence ontology’s core concepts.

Concept Summary of Axioms

Agent An entity that has a hasTask relation to a Task and a hasGoal relation to a Goal.

Task Has a taskOf relation with the Agent that has the Task, and the Goal that the Task is towards. May have Activity(s) associated with

it via a hasActivity relation.

Goal Deﬁned in terms of a goalOf relation with the Agent that has the Goal, and a hasDesiredState relation with the State that the goal

is about.

Activity Can be performedBy an Agent, is an activityOf a Task, and causes a State. Note that the State that is causedBy an Activity is an

Outcome. An activity may also be enabledBy a State.

ActivityDecomposition Is a decompositionOf an Activity and a compositionOf at least two other Activity entities (which are the subactivities of the decom-

posed activity).

ActivityCharacteristic An Activity may have a hasActivityCharacteristic relation with an ActivityCharacteristic. TemporalCharachterstic, SpacialCharac-

teristic, InputCharacteristic, ProcessCharacteristic, and OutputCharacteristic are types of ActivityCharacteristic.

Resource An entity that is either requiredBy or producedBy an Activity. Can be a Shareable or NonShareableResource, and a Consumable or

NonConsumeableResource.

State A State may either be a ComplexState or AtomicState. A ComplexState has at least one hasSubState relation with another State, and

may be either a ConjunctiveState or a DisjunctiveState. An AtomicState has a hasStateCharacteristic, hasOperator, and hasValue

relations with a StateCharacteristic, Operator, and Value, respectively.

BasisOfDependence Has a hasDependum relation with either a Task, Activity, ActivityCharacteristic, Resource, or StateCharacteristic. Can have con-

strains and alteredBy relations to a Task, Activity, or ActivityCharacteristic. Availability, Complementarity, Functionality, Compat-

ibility, Uncertainty, and Complexity are types of BasisOfDependence.

Figure 2) and demonstrate the type of insights it can

generate. Prover9 and OWL implementations of this

section are also provided in the Github repository, to

allow for independent veriﬁcation that our framework

supports generating the inferences described in this

section. Given space constraints, we focus on the ele-

ments that illustrate dependencies, rather than a com-

prehensive task structure. We will explore three in-

stances of dependencies, each at different abstraction

levels, to demonstrate the model’s ability to identify

dependencies at both well- and ill-deﬁned task struc-

ture levels.

The agents are the Riverine Flood Risk Mitiga-

tion Team (RM), and the City’s Water Infrastruc-

ture Management (WIM) and Transportation Services

(TS) departments. The neighbourhood of interest is

grappling with substantial ﬂood risks due to both ur-

ban (sewer overﬂows) and riverine (watercourse over-

ﬂows) sources. RM is designing a bridge as part of its

Riverine Flooding Project (RFP). WIM must redesign

a sanitary trunk sewer (STS) for capacity expansion

and execute the Urban Flooding Program (UFP) to

reduce urban ﬂooding. For the STS, WIM must de-

sign a new storage pipe and require road access for

UFP sewer improvements. TS must repave a road to

maintain good road conditions. Three dependencies

inferred here are:

Dependency 1. Flooding is a complex issue due

to riverine and urban ﬂooding being interconnected

sources. If sewer systems are improved to reduce

urban ﬂooding, but the region remains vulnerable to

a nearby overﬂow-prone watercourse (riverine risk),

ﬂooding will still occur. The ﬂood protection level

in a given area is only as strong as the weakest link.

Hence, there is a complementary basis of dependence

among tasks to reduce various types of ﬂood risk. Par-

ticularly, executing the UFP depends on the STS and

riverine developments, since the speciﬁc sewer im-

provements are constrained by the existing ﬂood risk

altered by these two developments.

Dependency 2. Co-location demands compatibil-

ity between the design of the RFP bridge and the

STS’s storage pipe. Although they don’t impact

each other’s functionality, their coexistence estab-

lishes a compatibility-dependent relationship. Since

the bridge design precedes the storage pipe design, it

alters this dependency and constrains the pipe design,

which must align with the bridge design.

Dependency 3. TS’s road paving and WIM’s sewer

improvement activities require access to the same

road, causing an availability-based dependency. The

start times of each activity alter and are constrained

by the availability, creating mutual dependence.

Based on the representation of these dependen-

cies, we are afforded the ability to reason about how

to address them. The UFP execution’s dependency

on the RFP and STS developments informs the con-

siderations WIM must make. For instance, post-

development hydrological models could guide the

UFP’s activities. The dependency between Activity 1

and Activity 2 necessitates information exchange on

the bridge design to facilitate a compatible pipe de-

sign. With Activity 3 and Activity 4 mutually depen-

dent due to timing, we see that coordination in terms

of scheduling is necessary, positioning a schedule as

a coordination mechanism. This awareness can also

help WIM and TS to synergistically overlap their ac-

tivities, reducing public disruptions and setup costs.

In summary, the model effectively encapsulates

the task environment at varied speciﬁcity levels, iden-

tiﬁes task and activity dependencies, uncovers depen-

dency roots, and suggests coordination methods.

7 DISCUSSION

In our discussion, we draw attention to three key, mu-

tually reinforcing beneﬁts of our framework: multi-

level abstraction, inferred dependencies, and en-

Towards an Ontology of Task Dependence in Organizations

105

Figure 2: Partial representation of three case study scenarios. Pink relations indicate dependencies that can be inferred.

hanced reasoning about coordination. The precise

semantics of our axiomatization, linking tasks and

activities through bases of dependence, allows auto-

mated identiﬁcation of critical and potentially latent

dependencies. By facilitating the discovery of depen-

dencies that might otherwise go unnoticed, coordina-

tion failures can be preempted and opportunities for

improved integration can be revealed, as exempliﬁed

by the third dependency outlined in Section 6.

As observed in our case study, many project ele-

ments required rework due to a lack of dependency

awareness. This could be avoided by taking advan-

tage at project design time of decision-support tools

that are able to alert decision-makers of potential in-

ﬂuences on or from the various project tasks. Our for-

mally axiomatized and implemented framework can

serve as the backbone of such tools.

Secondly, by distinguishing the intent to achieve

goals from the activities performed to reach them, the

model captures dependencies at various task abstrac-

tion levels, making it applicable in both well- and ill-

understood task environments. This makes our frame-

work practically useful for managing dependencies

across strategic, tactical, and operational tiers of pol-

icy, program, and project management when embed-

ded in a decision-support tool.

Finally, by expressing dependence through its ba-

sis and effect, our model facilitates a deeper under-

standing of coordination failures’ root causes and re-

veals potential mitigation strategies. It offers a new

perspective on coordination failures by treating them

as unsatisﬁed (or unoptimized) constraints (Herbsleb

and Roberts, 2006), with the basis of dependence

representing the unsatisﬁed constraint and the effect

revealing the “violating value”. Consequently, our

model can be viewed as a higher-level abstraction

of constraint networks, providing an explicit frame-

work for reasoning about strategies to “solve” con-

straint networks based on various activity character-

istics. For instance, while the set of dependsOn re-

lations between temporal activity characteristics can

be seen as forming a temporal constraint network, for

which several algorithms exist (Dechter et al., 1991),

we can now model constraint networks based on other

activity attributes, such as cost, design, and quality

networks. With this perspective, we can work towards

both human- and computer-based coordination strate-

gies for “solving” the various kinds of networks.

However, our framework, in its current form, also

has limitations. Primarily, the bases of dependence

are asserted, not inferred. Ideally, the existence of

all bases of dependence would be automatically in-

ferred based on a robust representation of task struc-

tures. For instance, while availability can be in-

ferred from the current axiomatization, complemen-

tarity cannot be, due to the present inability to repre-

sent “non-additivity” in state characteristics. There-

fore, a more detailed representation of activities and

states is needed to facilitate the automated detection

of bases of dependence. Moreover, the framework

does not support accounting for the relative impor-

tance of the various dependencies impacting a task or

activity, thus failing to explicitly capture the signiﬁ-

cance of a constraint amidst large networks of depen-

dencies with inherent trade-offs. These aspects call

for reﬁnement of our model in future work.

8 CONCLUSION

In this paper, we present a novel approach to repre-

senting tasks, task structures, and task dependencies

to help enhance coordination in organizations. Faced

with the coordination issues identiﬁed in real-life case

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

106

studies shared by domain experts, we examined the

organizational and enterprise modelling literature for

applicable modelling frameworks. We concluded they

did not meet the representational requirements we in-

ferred from our use cases. As such, we developed a

new model of task dependence, grounded in the no-

tion of constraint, and underpinned by two new con-

structs that deﬁne the basis and effects of a depen-

dency. We discussed a practical application of our

framework to a real-world scenario and showed how

it enables the inference of (latent) dependencies and

the reasoning about coordination strategies to address

them. In future work, we plan to extend the frame-

work and its ability to support organization design de-

cisions and facilitate the integration of work by tack-

ling the modelling of inter-agent dependencies.

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