Process discovery is a data-driven method for discovering the control flow of processes – the how of a process. Complementary to this, Decision Mining (DM) uses machine learning techniques to discover the decisional criteria used in the process – the why of a process. Such decisional criteria, also known as guards, reflect the reasons why cases are routed along branches at a decision point. We performed a literature review on existing DM approaches and their utilized algorithms, datasets, and tools. We provide an interim report of our review in this paper. Additionally, to make our review results more accessible to DM practitioners and researchers, we present a structured, machine-readable, queryable and interactive representation of our review results. The Systematic Literature Review Ontology on DM (SLRO-DM) allows capturing work in DM as a Knowledge Graph (KG), from multiple perspectives, including research problems, proposed solutions, related works, and decision types. The SLRO part of the ontology is extendable and reusable, so it may be adapted for creating literature KG in other domains. The goal of the DM-KG is to support (a) practitioners in the practical application of DM, and (b) researchers in their exploration of the research landscape and identification of gaps. We evaluated our ontology using competency questions that target both these stakeholders.