See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/342418045 CCOnto: Towards an Ontology-Based Model for Character Computing Chapter · June 2020 DOI: 10.1007/978-3-030-50316-1_34 CITATIONS READS 5 118 3 authors, including: Alia el Bolock Slim Abdennadher German International University and Ulm University The German University in Cairo 32 PUBLICATIONS 120 CITATIONS 246 PUBLICATIONS 1,941 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Arabic Named Entity Recognition View project CFP: 4th Workshop on Character Computing (Co-located with PAAMS'21) View project All content following this page was uploaded by Alia el Bolock on 09 February 2021. The user has requested enhancement of the downloaded file. CCOnto: Towards an Ontology-based Model for Character Computing Alia El Bolock1;2Q, Cornelia Herbert2, and Slim Abdennadher1 1 German University in Cairo, Cairo, Egypt falia.elbolock,[email protected] 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, af- fect, 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 nec- essary 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 com- plex. 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 ontol- ogy 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 hu- man 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, cog- nitive 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 situa- tion (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, be- havior, and character. As discussed above, for the purpose of this paper, we only consider the performance (behavior) of an individual within a specific task (sit- uation), 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, af- fect, 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 fo- cus 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 individu- als 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 on- tology 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 be- tween 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 Prot´eg´e5.2.0 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