When the Practice of Theorizing Meets the Theorizing of Practice. Social Knowledge Making in Organization Science Academia and Managerial Communities

The aim of the present work is to contribute to a better understanding of the relation between organization theory and management practice. It is organized as a collection of two papers, a theoretical and conceptual contribution and an ethnographic study. The first paper is concerned with systematizing different literatures inside and outside the field of organization studies that deal with the theory-practice relation. After identifying a series of positions to the theory-practice debate and unfolding some of their implicit assumptions and limitations, a new position called entwinement is developed in order to overcome status quo through reconciliation and integration. Accordingly, the paper proposes to reconceptualize theory and practice as a circular iterative process of action and cognition, science and common-sense enacted in the real world both by organization scholars and practitioners according to purposes at hand. The second paper is the ethnographic study of an encounter between two groups of expert academics and practitioners occasioned by a one-year executive business master in an international business school. The research articulates a process view of the knowledge exchange between management academics and practitioners in particular and between individuals belonging to different communities of practice, in general, and emphasizes its dynamic, relational and transformative mechanisms. Findings show that when they are given the chance to interact, academics and practitioners set up local provisional relations that enable them to act as change intermediaries vis-a-vis each other’s worlds, without tying themselves irremediably to each other and to the scenarios they conjointly projected during the master’s experience. Finally, the study shows that provisional relations were accompanied by a recursive shift in knowledge modes. While interacting, academics passed from theory to practical theorizing, practitioners passed from an involved practical mode to a reflexive and quasi-theoretical one, and then, as exchanges proceeded, the other way around.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.