Brian Donohue and J. Neil Otte
Fall 2013
Ontological Engineering
Prof. Barry Smith
THE FIVE-FACTOR MODEL AND PERSONALITY ASSESSMENT ONTOLOGY
1. Introduction
What factors lead to someone having a particular personality? If we know
someone’s personality, what can we predict about their lifetime income, social success,
and longevity? Is our personality fixed, or can we change it?
The history of the study of personality in the twentieth century has been one of
promise, disappointment, and rebirth. Initially, empirical psychologists proposed a
number of testable components of personality. One psychologist might propose scores for
thinking, feeling, sensing and intuiting; another might propose scores for extroversion
and harm dependence. This led to a variety of competing models with no clear winner,
and the impression that the study of personality was without a solid subject matter. As far
back as 1958, Gordon Allport complained, “each assessor has his own pet units and uses
a pet battery of diagnostic devices” (Allport 1958, p. 285).1 In the 1970s and 1980s, the
study of personality declined due to a lack of consensus, and new studies emphasizing the
role of circumstance and situation on individual action came to prominence.
But now researchers again have grounds for optimism. The Five-Factor Model,
also known as the Big Five Model of Personality, has brought new life into the study of
personality.
1
This quote is reported by Nettle (2007).
2
1.1. What Is the Five-Factor Model (FFM)?
The Five-Factor Model proposes that there is an exhaustive set of five personality
dimensions that sufficiently capture a person’s unique personality, which we understand
here to be a disposition of individual persons to respond similarly across a variety of
situations. The dimensions of FFM include: Openness to Experience, Conscientiousness,
Extroversion, Agreeableness, and Neuroticism (OCEAN, for short).2 Each of these
dimensions represents a range on which individual subjects may be tested: an
Extroversion score, for instance, will report an individual as falling somewhere between
extreme extroversion and extreme introversion. Each dimension may also be thought of
as tracking a unique set of traits and personality features that appear in descriptions of
individuals strongly under each dimension. For example, Openness includes features like
imagination and insight, and signifies a broad range interests. Conscientiousness
commonly includes high levels of thoughtfulness, good organization skills, and excellent
impulse control. Extraversion includes traits like edginess, amiability, confidence, and
emotional expression. Agreeableness includes benevolence toward others and other
prosocial behaviors. Neuroticism includes emotional distress, angst, irritability, and a
proclivity to depression or sadness.
Previous studies produced many testable characteristics, but lacked this core
system of traits. To counter this, psychologists began running factorial analyses on the
available data to see whether or not there were more primary characteristics underlying
those that previous researchers had proposed. This allowed them to uncover a hierarchy
2
I will begin a convention here of capitalizing dimensions to more clearly distinguish them from other
scores discussed.
3
of variables, of which many previous characteristics were merely synonymous or multicategorical measurements. What they found surprised them. Tupes and Christal (1961),
who found five recurrent factors in analyses of personality ratings in eight different
samples, wrote:
“In many ways it seems remarkable that such stability should be found in an area which to
date has granted anything but consistent results. Undoubtedly the consistency has always
been there, but it has been hidden by inconsistency of factorial techniques and philosophies,
the lack of replication using identical variables, and disagreement among analysts as to
factor titles” (p. 12).
As a result of these findings, FFM has become the dominant model in social
psychology, and its broad ability to correlate with various features of our lives—e.g.
marital success and longevity—make it a worthwhile model to pursue further research. In
addition, contemporary personality tests based on the Five-Factor Model are much more
reproducible, have greater universality, and offer a holistic picture of personality than
previous tests. A good example of this is the Newcastle Personality Assessor (NPA),
which asks twelve questions—a reduction of the longer and more accurate IPIP
questionnaire.3 Because the FFM bears a complex mathematical relationship to other
measurements of personality, its dominance also poses a particular project for linked
data—and this is our subject.
2. Five-Factor Model and Our Project
The discovery of these new surveys of personality leaves open questions about the
original survey data using other instruments and its relationship to the new model. For
3
See http://ipip.ori.org
4
example, when a researcher uses the Adjective Check List, they may test a subject’s
degree of self-ascriptions of terms such as anxious, self-pitying, tense, touchy, and
unstable, according to a Likert scale. Factorial analyses suggest that such terms are
tracking a real dimension of personality according to the Five-Factor Model:
Neuroticism. This discovery suggests that a score for anxiety in the Adjective Check List
can be counted as a score for Neuroticism according to the Five-Factor Model. McCrae
and Oliver P. John (1992) offer the following chart, which displays a sample of the kinds
of relationships we wish to capture. These include positive and negative correlations that
hold between the scores of various instruments and the factors of the Five-Factor Model.4
McCrae, Robert R. and John P. Oliver. “An Introduction to the Five-Factor Model and Its Applications.”
Journal of Personality. Vol. 60, Issue 2, pp. 175-215, June 1992
4
5
Figure 1: McCrae and John's depiction of personality instruments (inventories) and FFM
These relationships have been well known to personality researchers for decades,
but they are hidden in silos of data that exist in various forms: online databases, journal
archives, and individually published papers. Such large caches of personality studies
using various methods could be combined under the interpretation of the Five-Factor
Model to create a massively universal scoring system for all personality tests. This would
allow for inferences to be drawn across all studies under the ontology, alongside their
individual correlative variables, negating much of the need to reproduce previous work
under the same FFM instrument.
It is to this end that our project proposes an ontological model for rendering a
single set of results, defined by the Five-Factor Model, for all instruments of personality,
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which in our ontology we refer to as “inventories.” Our model uses the familiar, widely
used Basic Formal Ontology (BFO) 1.0 as an upper-level ontology, and connects
instruments and the scores they generate to particular factors in FFM of particular
persons. The resulting ontology is missing necessary components to render reliable
determinations of evidence between other instruments and the FFM. But it is our hope
that this ontology is a proof of principle, which with further development could be used
to organize multiple databases into a single, searchable resource using an RDF data
model and the SPARQL query language.
3. Utilized and Related Ontologies
Our project, Personality Assessment Ontology (PAO), either utilizes or relates in
potentially significant ways to a few other ontologies. Below, we explicate how PAO
utilizes the content and structure of Basic Formal Ontology and Neuropsychological
Testing Ontology (NPT) in order to represent the factorial analyses of personality
dimensions. Also, we discuss the ways in which PAO relates to Mental Functioning
Ontology (MF) and Emotion Ontology (EMO). Thus, with respect to PAO, BFO and
NPT count as utilized ontologies, whereas MF and EMO count only as related
ontologies. These latter ontologies show similar concern for representing the information
of interest to the psychological sciences, but their entities, classes, etc., are not directly
incorporated into the architecture of PAO.
3.1 Basic Formal Ontology
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PAO was built from the top down, as it were, according to the framework set up
by the Basic Formal Ontology. Of particular importance were the basic classes of
Process, Person, Role, Disposition, and Generically Dependent Continuant. Since PAO
aims at capturing and consolidating the process of personality assessment, it utilizes
BFO’s distinction between continuant and occurrent entities. Furthermore, two of BFO’s
realizable entities, Role and Disposition, provided a few crucial distinctions for some of
the foundational entities within PAO: subjects of assessment are persons in the role of
evaluant, and personalities are dispositions belonging to and realized by persons (or
persons’ behavior). Finally, under the BFO class of Generically Dependent Continuant,
PAO is capable of representing the data produced by personality assays as subclasses of
Information Content Entity. As we describe below, each assay of personality represents a
distinct data item with a definite numeric value as determined by the directives given to it
by the assay’s developers.
3.2 Mental Functioning Ontology and Emotion Ontology
In part, PAO was inspired by the work accomplished already in the Mental
Functioning Ontology (MF) and the closely related Emotion Ontology (EMO). These
ontologies seek to encapsulate the functional, characteristic, and responsive aspects of
human psychology. This links the broader projects of MF and EMO with the narrower
one of PAO, since much of the assessment portion of PAO will constitute, in effect, an
assessment of the aspects of psychologies depicted within MF and EMO. Nonetheless,
many of the other facets of MF and EMO are largely irrelevant to the basic structure of
PAO. There is no need, at this stage in PAO’s development, to represent discrete classes
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of psychological acts performed by a subject (e.g. cognitive appraisal or affective
representation), even if the elements of these classes ultimately form the psychological
mosaic that constitutes a personality type. It is by no means inconceivable that further
ontological work could forge a firmer link between MF and EMO on the one hand and
PAO on the other. That being said, far more extensive research would be required to
realize this, especially in characterizing the transition from isolated psychological acts to
a settled, though impermanent, psychological disposition.
3.3 Neuropsychological Testing Ontology
Among all the related and utilized ontologies, PAO draws foremost from the
structure and resources of the Neuropsychological Testing Ontology (NPT).5 NPT
represents a more general range of neuropsychological assessments, including memory,
learning, attention, and expression. Along similar lines, PAO includes a list of personality
scales that test for habitual behavioral and emotional patterns. In NPT, these assays
provide measurement data about cognitive functions. Likewise, in PAO, the personality
assessments provide measurement data about personality dimensions. Borrowing from
NPT, the assays in PAO are planned processes that realize a plan that concretizes specific
directive information.
4. Personality Assessment Ontology (PAO)
In the interest of representing the full process of personality assessment, we
divided our project into three main tasks: (4.1) the representation of roles involved in the
5
We are indebted to Barry Smith for directing us to this ontology.
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assessment process, (4.2) the representation of particular processes of assessment, and
(4.3) the representation of data accumulated from that process.
4.1 Persons, Roles, and Dispositions
The aim of PAO is to represent the data produced through the process of assessing
various dimensions of personality. However, personalities are not free-floating entities;
they inhere in persons, who are the subjects of assessment. Therefore, the first entities
that we sought to represent were persons, roles, and the personality belonging to a person.
Along with ‘evaluant,’ i.e. a person in the role of undergoing some assessment, we
included the role of ‘evaluator,’ i.e., the role of the person who implements the plan of
assessment. As for classifying Personality, we mimicked the practice of NPT: where NPT
counted various cognitive functions as subclasses of Disposition, we counted Personality
as a subclass of Disposition.
Alongside Personality, we identified the class, Personality Dimension. A
Personality Dimension is not a subclass of Personality (since, by the “true path” rule, this
would render each Personality Dimension as some Personality, which is false), but rather
each Personality Dimension is a part of Personality. The subclasses of Personality
Dimension we identified according to FFM: Openness to Experience, Conscientiousness,
Extraversion, Agreeableness, and Neuroticism. Each of the five is a part or facet that
helps make up the total Personality, which is a Disposition of the Person toward certain
behaviors and beliefs.
4.2 Assays
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Our second task was to represent the process of personality assessment such that
disparate inventories and instruments could be interpreted as assessments of the five
subclasses of Personality Dimension. We developed the class Assay as a subclass of
Process and its subclass Planned Process (the latter borrowed from NPT). Under Assay,
we created a subclass of assays (Personality Scale) specified to assess Personality, and
then included subclasses of entities that were themselves specified assays, e.g. Likability
Assay, Judgment Assay, and Well-Being Assay. Each subclass of Assay is drawn from
the sample of personality inventories included within PAO. Both the full inventories (e.g.
Myers-Briggs Type Indicator and Hogan Personality Inventory), and the individual parts
of those inventories (e.g. Likability Assay is part of Hogan Personality Inventory), are
included here as subclasses of Personality Scale, since both the full inventories and their
parts assess Personality. Finally, each specified personality assay has as its output some
score that represents the degree of presence or absence of Personality factor (see 4.3
below).
Following NPT, we counted the assays as realizations of a Plan Specification,
which is a subclass of Directive Information Entity, a subclass of Information Content
Entity (BFO). This included as parts Action Specification (i.e. actions to be performed by
the evaluator and evaluant) and Assay Objective Specification. (Recall that Personality
Scale is a subclass of Planned Process.)
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Figure 2: A personality scale and its parts
4.3 Score Data and Values
The final task was to represent in PAO data values as the specified outputs of
personality assessments. We needed to be able to represent, for example, whether the
output of a Personality Scale recorded that an evaluant’s personality is in part
characterized by high or low Conscientiousness. It became evident that PAO required the
creation of some subclasses of information or data entities.
To this end, we created a class of Scalar Personality Measurement Datum, which
is a subclass of Information Content Entity, which is in turn a subclass of Generically
Dependent Continuant (BFO). Under Scalar Personality Measurement Datum, we created
only five subclasses of data entities to match FFM. This was a significant step, since
PAO is claiming (perhaps not uncontroversially) that each measurement produced by the
wide variety of personality inventories just is measuring the dimensions identified by
FFM. In building the ontology, the question arose of whether we should create a class of
data entities to match each assay (e.g. Likability Assay has specified output Likability
Score), and then stipulate that the Likability Score should undergo data transformation to
become an Agreeableness Score. We opted against this, since (a) we wanted to avoid
introducing data transformation in a case in which the pools of data were already largely
amenable, and (b) we wanted to stand behind the strong claim, in accordance with
McCrae & John (1992), that a measurement of Likability produced by the Likability
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Assay just is a measurement of Agreeableness on FFM. Thus, in PAO, a Likability
Assay, which is part of the Hogan Personality Inventory, produces a Likability Score, but
this score is a subclass of Agreeableness Score, and both are treated as measuring the
Agreeableness of some Personality. At the same time, we excluded separate personality
dimension entities corresponding to each personality inventory (e.g. Likability as a
subclass of Personality Dimension).
After identifying the output of personality assessments as data entities (scores),
the problem of representing the specified values of these scores remained. Thus, we
created the data property: ‘has score of.’ That is to say, the data entity (Generically
Dependent Continuant), the score that is the output of the assessment, has the data
property ‘has score of’, which is a definite numeric figure. This figure, we stipulated, has
a maximum cardinality of 100 and a minimum cardinality of 1. In turn, the wide variety
of inventories and questionnaires can be translated into roughly a percentage scale simply
through dividing the total positive answers in the numerator by the total possible answers
in the denominator:
𝑥=
𝑛
𝑠
For instance, in a questionnaire consisting of fifteen questions assessing likability,
an evaluant who answers thirteen of these questions positively receives a Likability
Score, and thus an Agreeableness Score, of 87%. In theory, this translation is possible
regardless of the psychometric mechanism that the questionnaire employs, for instance,
whether the assay uses a Likert scale, or self-ascriptions from an adjective list. However,
it should be noted that the current version of PAO does not contain any representation or
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annotation for precisely how transformations of variegated kinds of scales would take
place. Furthermore, there is no indication of the significance of each score (see below).
Figure 3: An assay outputs a score
At this stage in its development the ontology faces a sizable difficulty. Although
the majority of the personality assays correlate positively with the assessed personality
dimensions (e.g. some Likability Assay with an output of 70% represents that the subject
has 70% Agreeableness), a handful of the assays correlate negatively with the personality
dimensions (e.g. some Well-Being Assay with an output of 30% represents that the
subject has 70% Neuroticism), namely: Aggression, Critical Parent, Paranoid, Social
Introversion, Adjustment, Ideal Self, Objectivity, and Well-Being. Within Protégé 5.1,
we could find no tool by which to automate the inversion of the scores generated by the
assays. We considered forcing each output of each assay, whether positive or negative, to
undergo data transformation, but this seemed either unnecessary or misleading in the case
of positively correlated data. Thus, we decided to create a separate data property, ‘has
negatively correlated score,’ and annotate the inverse data property to indicate the sort of
transformation the assay score (n) must undergo in order to count as a measurement of
some Personality Dimension (x):
14
𝑥 = 100 − 𝑛
This is a patchwork fix to the architecture of our ontology to be sure. But we
cannot at present conjure up a better solution.
Figure 4: Neuroticism Score and its subclasses
5. Conclusions and Further Research
At present, our model captures the relation of evidential support between
instrumental measurements and the FFM, but treats the nature of that support rather
crudely as equivalent percentages recorded as measurements of dimensions. This is
clearly inadequate. Not all positive correlations between a score and a personality
dimension will maintain an equivalent score. This is also true of negative correlations.
And we have presently no means by which to distinguish the statistical significance of
one type of instrument versus another, let alone specific applications of an instrument.
To make this ontology useful, information concerning the individual instruments
would need to be accounted for in the ontology. Such information would need to include
the number of questions and the kind of questions asked (e.g. true-false, Likert scales,
self-descriptions), along with the nature of the correlations to FFM. There is also
information that would be required from individual applications of each measurement.
This information would include basic statistical information about each study such as
population size and standard deviation, as well as significance and power, but also
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information such as geographical location, the name of the evaluator who conducted the
study, and a timestamp. This kind of information would be especially important for
determining the plasticity of personality over time, and the effects of socio-economic
status on displays of typical behaviors associated with personality types.
5.1. Auto-Diagnostic Inference
Auto-diagnostic inference would be possible if other indicators of personality
types and thresholds could be incorporated into the ontology. Fascinating studies
correlating personality types and lifespan, success at work, and marital bliss could be
employed to infer individual diagnostic profiles for persons based on their responses to
individual tests. This kind of information would not be particular to our ontology, but to
any ontological mapping of the FFM. What would be unique to our approach would be
the granularity achieved by our ontology, which would be able to draw inferences across
test types with distinctive purposes and from times in the past before the FFM was
available. The promise of our model is its ability to extend the potential of the available
data to avoid reinventing the wheel.
5.2 fMRI, Ontologies, and the Need for Further Research
Part of the resurgence of interest in personality stems from significance advances
in neurosciences and the ability to track blood flow through the brain using fMRI. As
Nettle (2007) accurately observes, “a new science is emerging of individual differences
in brain structure and functioning and the results of this science can be mapped back to
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the big five personality dimensions.”6 The mission to incorporate emerging neuroscience,
social psychological research, and genetic discovery is a massive task. If it is to be
successful, the ontologies describing neurological and cognitive functioning that have
been characterized here, as well as the one we propose here, will need to be incorporated
to capture new results. This makes the use of a proper upper-level ontology like BFO
especially important. If ontologies built for distinct purposes are to be successfully
merged, they must be interoperable.
PAO raises multifarious questions as it answers them. It provides only a
preliminary solution to the problem of amassing pools of variegated personality
assessment data. Nonetheless, this step is an indispensable one. The advances within
personality psychology procured by factorial-analytic method, the fruit of which is the
Five-Factor Model, clear the way for further research, e.g. the correlations of health and
mental disorder with shifts in personality dimensions. Personality Assessment Ontology,
insofar as it supplies a model of a panoply of personality assessments, and insofar as it
does so successfully, opens one avenue toward an extended, more comprehensive system
of ontologies of mental functioning and mental health.
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p.11
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Works Cited
Allport, G. W. (1937). Personality: a psychological interpretation Holt. New York, NY.
McCrae, Robert R. and John P. Oliver. (June 1992) “An introduction to the five-factor
model and its applications.” Journal of Personality. Vol. 60, Issue 2, pp. 175-215
Nettle, Daniel. (2007) Personality: what makes you the way you are. Oxford University
Press. New York, NY.
Tupes, E. C , & Christal, R. E. (1961). Recurrent personality factors based on trait
ratings (USAF ASD Tech. Rep. No. 61-97). Lackland Air Force Base, TX: U.S.
Air Force.