Cconto: Towards an Ontology-Based Model for Character Computing

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CCOnto: Towards an Ontology-Based Model for Character Computing

Chapter · June 2020 DOI: 10.1007/978-3-030-50316-1_34

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Alia El Bolock1,2Q, Cornelia Herbert2, and Slim Abdennadher1

1 German University in Cairo, Cairo, Egypt {alia.elbolock,slim.abdennadher}@guc.edu.eg 2 Ulm University, Ulm, Germany [email protected]

Abstract. Our lives are rewritten by technology and data, making it crucial for machines to understand humans and their behavior and react accordingly. Technology systems could adapt to different factors such as affect (Affective Computing), personality (Personality Computing), or character (Character Computing). Character consists of personality, affect, socio-cultural embedding, cognitive abilities, health, and all other attributes distinguishing one individual from another. Ontology-based conceptual models representing individuals i.e. their character and re- sulting behavior in situations is needed for providing a unified framework for building truly interactive and adaptive systems. We propose CCOnto, an ontology for Character Computing that models human character. The ontology is to be used for adaptive interactive systems to understand and predict an individual’s behavior in a given situation, more specifically their performance in different tasks. The developed ontology models the different character attributes, their building blocks, and interactions with each other and with a person’s performance in different tasks.

Keywords: Character Computing · Ontology · Personality · Aﬀect

1 Introduction

Nowadays, where technology and data are an integral part of our lives, it is necessary for machines to understand humans to predict and adapt to their behavior more now than ever. Accordingly, frameworks for developing adaptive systems are in high demand. Many approaches for adapting to affect and personality already exist. While Affective Computing [16] and Personality Computing [19] focus on affect and personality, respectively, Character Computing [10, 12, 8, 13] advocates that affect and personality alone are not enough to capture the essence of a person and their behavior. Modeling affect and personality on their own is a complex task. However, adding other factors to it (e.g. culture, health) as well as distinguishing between different situations, makes it exponentially more complex. Developing conceptual models, i.e. ontologies, is one often used approach for representing and modeling such a complex interaction. Several approaches have been proposed for using ontologies in similar endeavors related to human 2 El Bolock et al. personality, emotions, and behavior. For example, EmOCA, an emotion ontology can be used to reason about philia and phobia based on emotion expression in a context-aware manner [5]. EmotionsOnto is another emotions ontology for developing affective applications and detecting emotions [3]. In [2], an ontology of psychological user profiles (mainly personality traits and facets) is presented. LifeOn is an “ubiquitous lifelong learner model ontology” (with a highlight on learner personality) for adaptive learning systems [15]. An ontology for insider threat risk detection and mitigation through individual (personality, affect, ide- ology and other similar attributes) and organizational sociotechnical factors is presented in [14]. For different extensive overviews of ontologies related to human behavior and affective states refer to [1, 6]. An ontology of human character is necessary to enable machines to understand people and people to understand themselves and each other. It also provides a unified foundation for building adaptive systems that interact with users, moving persons further to the center of computing. The character is the individual person with all his/her defining or describing features, such as stable personality traits, variable affective, cognitive and motivational states, history, morals, beliefs, skills, appearance, and socio-cultural embeddings. However, the character cannot be understood alone but rather has to be investigated through its effect on behavior in a given situation (denoted the Character-Behavior-Situation (CBS) triad [9]). The developed ontology, CCOnto, serves as a formal foundation for understanding and shar- ing knowledge about human character and its interactions. It is also a unified, reusable knowledge base which can be leveraged in building various adaptive or interactive systems within the framework of Character Computing (see [11]). The ontology model is based on the behavior of a specific individual in a given situation. Currently, the situation is constrained to performing specific tasks and the behavior is measured as the score or the performance within these tasks.

2 CCOnto Model

The model of CCOnto distinguishes between three main concepts: situation, behavior, and character. As discussed above, for the purpose of this paper, we only consider the performance (behavior) of an individual within a specific task (situation), measured by a score. The person is the central concept of the ontology relating all the others together. One can think of it in terms of a person with character x performing task y (situation) and has score z (behavior). The x in turn consists of many components x1, x2, ..., xn representing the personality, affect, emotion, culture, etc. Based on the different character attributes, persons can be further categorized into different subsets, as will be discussed below. The character attributes can be divided into two sets of groups: stable traits and variable states. Most of the states have trait counterparts e.g. affect (trait) and emotions (state) or general and current health. We represent these attributes through the same concepts (classes) and only distinguish between them through different properties (representing the stable and variable counterparts). We focus on the more commonly represented components to be able to compare the CCOnto: Towards an Ontology-based Model for Character Computing 3 results to other work and evaluate CCOnto. As such, the top-level concepts for these character attributes are added into the ontology without going into their representation details. The most relevant character traits and states that are ex- tensively represented in the CCOnto ontology are personality traits, affect, and emotions. Initial steps to support cognitive capabilities, socio-economic standard, and culture are also taken. CCOnto distinguishes between person “types” based on certain character components which eases querying the ontology and applying rules to it which is needed for any application. Distinguishing persons based on personality traits (e.g., extrovert, introvert or energetic, laidback) is taken from the Personality Insights project by IBM. We also distinguish between individuals based on culture and age. The ontology is developed in a modular manner, enabling the addition of any further models representing the existing character attributes or adding new ones. The ontology design is based on common ontology development practices and makes use of already existing ontologies: the ontology of psychological terms [4] and the EmOCA ontology [5].

3 CCOnto Implementation

Fig. 1. An overview of character. The entities integrated from EmOCA are depicted in a diﬀerent color. The dotted boxes represent components making up the header concept. Not all entities of character are represented due to space constraints.

The main purpose of this work is to provide a generic model ontology of character for developing applications that can model and adapt to a person’s character when performing a specific task. The classes, and the properties between them, are derived based on our Character Computing model developed by the team of computer scientists and psychologists based on the research litera- ture. The ontology is implemented using Protégé5.2.0 and OWL 2. One main 4 El Bolock et al.

Class Subclasses - Individuals Trait Openness - highOpenness, lowOpenness Conscientiousness - highConscientiousness, lowConscientiousness Extraversion - highExtraversion, lowExtraversion Agreeableness - highAgreeeableness, lowAgreeableness Neuroticisms - highNeuroticisms, lowNeuroticisms Facet Imagination - highImagination, lowImagination Emotionality - highEmotionality, lowEmotionality Adventurousness - highAdventurousness, lowAdventurousness ArtisticInterests - highArtisticInterests, lowArtisticInterests Intellect - highIntellect, lowIntellect Liberalism - highLiberalism, lowLiberalism Emotion Anger, Disgust, Fear, Happiness, Sadness, Surprise (same naming for classes and individuals, for diﬀerent modeling purposes) Task GfTask - Form Boards, Paper folding, Spatial Relations, Letter Sets, .. GcTask - WJ Picture Vocab, WAIS Vocab, Antonym Vocab, .. MemoryTask - Logical Memory, Paired Associations, Free Recall SpeedTask - Digit Symbol, Pattern Comparison, Letter Comparison

Table 1. A fragment of the classes representing traits, facets, emotions and tasks. Due to space constraints some subclasses/individuals were not included.

Fig. 2. An overview of the different human categories based on their character attributes. The categorization is based on personality traits, facets, culture, and age. CCOnto: Towards an Ontology-based Model for Character Computing 5 advantage of using ontologies is ontology reuse. Of special relevance was the EmOCA ontology [5] from which the classes Person, Trait, Impact and Emotion were taken as a basis for modeling the interaction between the abstract version of persons with traits and emotions. Fig. 1 shows EmOCA’s main classes and how they were extended and embedded into the CCOnto ontology. The classes were integrated into the character hierarchy by specifying the emotion as part of the character and the trait as a super concept of the different personality traits available in the included models. All concepts that are already available in the APA Psychology Terms [4] ontology were reused to ensure inter-operability. Table 1 details the constructs that make up character, personality and emotion/affect, respectively. The categorization of the different implemented task types is integrated from [18]. To represent the score of every individual in a specific task i.e. the tertiary relationship between person, task and score, the class TaskScore is added. The different types of persons are shown in Fig 2. We define which classes are disjoint with each other e.g., Introvert and Extrovert, which is necessary when querying or reasoning on the ontology. The relationships and interactions between the character attributes are represented through properties. All main relations related to character are shown in Fig. 1. has impact, pertains to, is defined by and has trait have been integrated from the EmOCA ontology. The behavior of the first three properties is the same as in the original ontology, which serves to represent emotions as they result from stim- uli and are impacted by the personality traits of the Five Factor Model (FFM) [7]. To differentiate between stable trait-like emotions i.e. affect and variable state-like emotions we have the hasAffect and hasEmotion which both map a person to an emotion (either discrete or continuous). The has trait property has seven sub-classes representing the currently implemented traits (five from the FFM and two from the BIS/BAS [17]). The individual trait properties also indicate personality facets by mapping a person to the individuals of type facet (e.g. x hasExtraversion ‘‘highCheerfullness’’). Other important properties are the belongsTo and ofTask which map a TaskScore to a person and a task respectively and the Task’s data property hasScore which maps it to a score (xsd:int). Additionally, a person has hasCulture and hasAge properties mapping to (one or many) Culture individual(s) and an age (xsd:int).

4 Conclusions and Future Work

We presented an ontology of character from the perspective of Character Com- puting. The main focus of the ontology was modeling personality and affect, alongside other character components and their relation to a person’s performance in a specific task. The ontology is to be used for developing character- aware adaptive applications. The developed ontology can then be integrated into and extended for the purposes of any application that puts the individual at the center by considering different characteristics and their impact on the person’s behavior. More models representing the included and remaining character attributes can still be added to CCOnto. Character-specific visualization 6 El Bolock et al.

and editing capabilities need to be added to facilitate the use by domain experts. The ontology should be applied to multiple adaptive and predictive applications and use-cases (validated by both Computer Scientists and Psychologists). This would enable us to evaluate the model and extend it accordingly.

References

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