This manuscript "Knowledge Engineering for Wind Energy" addresses the challenges faced by wind energy experts in managing their data and developing AI-enabled systems. These challenges include understanding unfamiliar terms such as data schema, relational data-model, and metadata, selecting appropriate data storage solutions, and grasping concepts related to schema/ontology development, logic, and semantic networks. Additionally, wind energy experts must navigate a complex technology stack that includes various data formats and Semantic Web Technologies.

The paper presents the main concepts and summarizes previous work in knowledge engineering and representation in a manner accessible to wind energy domain experts. It provides insights applicable not only to the wind energy sector but also to other domains undergoing digitalization.

The paper is structured into several sections: Section 2 discusses the scope of knowledge engineering activities and common roles in the context of digitalization. Section 3 presents an overview of the knowledge representation problem and introduces basic concepts of the knowledge engineering domain. Section 4 focuses on practical technological implementations that enable the adoption of knowledge representation solutions, with a specific emphasis on Web technologies. Section 5 explores how knowledge representation technologies and knowledge engineering techniques facilitate the development of AI systems, particularly AI-enabled digital twins. Section 6 provides a systematic overview of current initiatives by the wind energy community in the knowledge engineering domain. Finally, Section 7 offers recommendations for fostering a healthy and thriving wind energy knowledge engineering ecosystem, followed by concluding remarks in Section 8.

Overall, the research topic and analysis of the manuscript is of a great interest of the wind energy industry. This research contributes valuable insights and guidance for the wind energy knowledge engineering.

The research is well-structured, the results are clearly presented, and useful examples are provided. I hereby recommend this manuscript to be published as is. I believe this research work will deliver a great contribution to the wind energy industry.

This study, "Knowledge Engineering for Wind Energy", focuses on the challenges in managing wind energy data and making it ready for the next-generation AI systems. The manuscript covers the current state-of-the-art knowledge engineering through an extensive review and at the same time identifies the problematic areas. The study reveals the importance of knowledge engineering for the digital transformation of the wind energy sector.

The study helps understand the current state-of-the-art by presenting distinct definitions (e.g.: the roles and activities of AI system design, development, and deployment tasks), and examples that make it clear to understand the complex concepts for the wind energy domain experts that are not necessarily experts in knowledge engineering. Pros and cons of common concepts are presented as well as the recently proposed concepts making this manuscript a strong candidate to become a key publication in this area.

In the final stage recommendations for further development and improvement are given. It is stressed that for progression a collaborative effort within the wind energy community as well as leveraging insights from other sectors that have already progressed in digital transformation are needed.

This manuscript is very informative and easy to read. The research is well structured, the review results as well as the recommendations are well presented, and clear examples are provided. In my opinion, this manuscript can be published without further revision.