Dilek Yargan

Big Data is believed to be one of the most important phenomena of the century due to its transformative effects and those undeniable effects of the data deluge in the fields of industry, science, and the Web -the fundamental wheels upon which today's world makes progress-are, however, hardly transformative. If Big Data is to lead to revolutions in these fields, the machine must be autonomous, that is, to perform higher-order cognitive skills, such as making decisions, inferences, and recommendations. The condition for an autonomous machine is its ability to understand and process Big Data. So, this work aims to determine the foundations for machine-understandability and accordingly determine the conditions for an autonomous machine. In this respect, this work proposes a machine ontology, Ontology 4.0, which represents phenomena in terms of their semantic properties in a relation-based fashion and processes semantic properties according to their types. Adapted from trope and type theories, this ontology forms the basis for machine-understandability. However, due to computational limitations from type theories, the semantic properties are discussed to be represented with nonextensional ontological objects, viz., urtropes, which turn out to be the building blocks of Ontology 4.0. Category theory is adapted as a formalization tool to implement this structure in the machine. Consequently, providing basis for machine-understandability, Ontology 4.0 is a machine ontology that harmonizes urtrope and modifed category theory.

It is an influential position in engineering design theory that one of the most important steps in conceptual design is the search for working principles. There is, however, no clear statement in the engineering literature about what a working principle is and how knowledge about working principles can be accounted for ontologically. We propose that dispositions are at the core of working principles, and thus that knowledge about working principles is knowledge about dispositions, their material bearers, and their triggers. Building on the engineering design framework developed by Pahl and Beitz, we suggest ontology design patterns for representing knowledge about working principles in a BFO-conformant manner. To represent trigger classes, we introduce the relation has trigger and its inverse trigger of, and discuss their limitations.

Engineering and other areas from industry are increasingly of interest from the perspective of applied ontology. While several ontological approaches have been proposed to annotate the engineering design process, alignment with the Basic Formal Ontology (BFO) and the principles of the Open Biological and Biomedical Ontologies (OBO) Foundry remains largely absent. This paper addresses this gap by proposing an ontological representation of the product development process in engineering, with a particular focus on the conceptual design phase, based on Pahl and Beitz's wellknown engineering design approach. Based on the Information Artifact Ontology (IAO) and the Ontology of Biomedical Investigations (OBI), we develop a representation that accounts for the information content entities and processes central to engineering design. The paper reviews existing design ontologies, outlines our development methodology, and presents a BFO-compliant ontological representation of the conceptual design process. The proposed approach supports integration with domain ontologies that represent technical artifacts.

The holy grail of Artificial Intelligence (AI) is to transform the machine into an agent that can decide, make inferences, cluster the contents, predict, recommend, and exhibit similar higher cognitive faculties. The prowess of Large Language Models (LLMs) serves as evidence: they enable seamless natural language communication and widespread use across various fields by swiftly processing unstructured data and handling diverse datasets with agility. However, in order to be competent in the fields of science and industry, an agent with such capabilities must be reliable, i.e., accountable for its decisions and actions, which is a per se attribute of an autonomous agent. In this respect, this paper aims to determine whether state-of-the-art technologies have already created an autonomous explanatory agent or are paving the way for the machine to become an autonomous explanatory agent. To achieve this, the paper is structured as follows: The first part investigates the types and levels of explanations in explanation models, providing a foundation for understanding the nature of explanations in everyday life. The second part explores explanations in the context of artificial intelligence, focusing on types of explanatory systems in the research field of eXplainable AI (XAI). The third part delves into whether and to what extent the state-of-the-art machine learning models function as autonomous explanatory agents, based on the exploration in the second part and considering the field of Human-Computer Interaction.

Veri bilimi günümüzün en önemli uğraşlarından biri sayılmakta, veriye verilen önem ve değer günden güne artmaktadır. Bu durumun ardındaki neden makinelerin veri depolama, toplama, üretme ve işleme kapasitesindeki olağanüstü artıştır. Enformasyon sistemleri makinelerin bu yetilerinden faydalanarak bilgi üretimine makineleri dahil etmek için çeşitli modellemeler geliştirmektedir. Ancak, veri yapılandırmasındaki çeşitli esneklikler modellerin değiştirilmesi ve/veya geliştirilmesi süreçlerinde sıkıntılara neden olmaktadır. Oluşabilecek kavramsal, teorik ve pratik uyumsuzlukları çözmek hedefiyle, felsefi bir uğraş olan ontoloji bilgi temsilinde ortaklık oluşturması için bilgisayar ve bilişim bilimlerinde, öncelikle enformasyon yönetimi sistemlerinde kullanılmaya başlanmıştır. Ontolojiler, temel olarak, bir alanın bilgisinin standardizasyonunu sağlamak için kurulurlar. Ancak her bilim kendi sorusu etrafında çalışmalar yaptığından farklı ontolojik seçimler standartlaşmalarda uyumsuzlukları beraberinde getirir. Bu nedenle, bilimsel ontolojik seçimlerinde ortaklaşma sağlayacak bir sisteme, yani tam da felsefi ontolojilerdeki gibi varlığa ait en üst kategorileri standartlaştıran bir sisteme ihtiyaç vardır. Yazımızda bu standartlaştırmanın, yani üst düzey ontoloji inşasındaki seçimlerin felsefi temellerini inceleyeceğiz. Ardından, belirli felsefi yaklaşımlara göre bir üst düzey ontoloji oluşturacağız.

The holy grail of Artificial Intelligence (AI) is to transform the machine into an agent that can decide, make inferences, cluster the contents, predict, recommend, and exhibit similar higher cognitive faculties. The prowess of Large Language Models (LLMs) serves as evidence: they enable seamless natural language communication and widespread use across various fields by swiftly processing unstructured data and handling diverse datasets with agility. However, in order to be competent in the fields of science and industry, an agent with such capabilities must be reliable, i.e., accountable for its decisions and actions, which is a per se attribute of an autonomous agent. In this respect, this paper aims to determine whether state-of-the-art technologies have already created an autonomous explanatory agent or are paving the way for the machine to become an autonomous explanatory agent. To achieve this, the paper is structured as follows: The first part investigates the types and levels of explanations in explanation models, providing a foundation for understanding the nature of explanations in everyday life. The second part explores explanations in the context of artificial intelligence, focusing on types of explanatory systems in the research field of eXplainable AI (XAI). The third part delves into whether and to what extent the state-of-the-art machine learning models function as autonomous explanatory agents, based on the exploration in the second part and considering the field of Human-Computer Interaction.

The history of ontology reveals various methodologies that examine being. Traditional ontology studies being qua being and categorizes it. Formal ontology determines the categories that are common to all entities and classifies them with formal languages using these categories as well. However, for over thirty years, formal ontologies have been studied and built outside of philosophy. The reason why ontology is separated from philosophy and becomes an interdisciplinary study is due to our need to make classifications and standardization in data and information management in accordance with reality. What lies behind this requirement is the exponential growth of data and the impossibility of analyzing it neither by humans nor by existing methods. For instance, dozens of studies are published every minute, and then scientists cannot follow all these developments with classical techniques. Therefore, the desire that machines should be involved in the processes of sharing and producing knowledge is far higher than ever. This desire, thus, has led us to develop new models for knowledge production. It has been experienced that the standardization of knowledge is essential in the construction of models; for, significant differences were found between taxonomic structures, concept definitions, and different granularity levels in the knowledge representations of various working groups, and even the integration of data was not possible. At this point, the selection, classification, and standardization of knowledge have been guaranteed by using ontological principles and methods. In this article, within information systems, we will introduce the formal ontologies, the formal theory covering the broadest knowledge of the field by selecting, classifying and standardizing it by using the principles and methods of philosophical ontologies. We will mention that we use description logics, a family of formal languages with the expressive power of natural language and high-performance reasoning algorithms, to represent this theory in machines. In essence, this article will introduce formal ontologies and description logics in the realm of knowledge representation.