# Knowledge Engineering for Wind Energy

arXiv: https://arxiv.org/abs/2310.00804 · HTML: https://mlsanity.com/paper/2310.00804/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 137 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries](https://mlsanity.com/paper/1910.09017/) (2019)
- [Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes](https://mlsanity.com/paper/2009.04547/) (2020)
- [Co-Design of Embodied Intelligence: A Structured Approach](https://mlsanity.com/paper/2011.10756/) (2020)
- [OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google's Knowledge Graph](https://mlsanity.com/paper/1805.03885/) (2018)
- [OntoWind: An Improved and Extended Wind Energy Ontology](https://mlsanity.com/paper/1803.02808/) (2018)
- [Dynamic Operads, Dynamic Categories: From Deep Learning to Prediction Markets](https://mlsanity.com/paper/2205.03906/) (2022)
- [Open Dynamical Systems as Coalgebras for Polynomial Functors, with Application to Predictive Processing](https://mlsanity.com/paper/2206.03868/) (2022)
- [An Energy Ontology for Global City Indicators (ISO 37120)](https://mlsanity.com/paper/2008.04070/) (2020)
- [Dialectica Petri Nets](https://mlsanity.com/paper/2105.12801/) (2021)

## Abstract (arXiv)

With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain, as well as from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating it with other sources of knowledge, and making it available for use in next generation artificially intelligent systems. To this end, this article highlights the role that knowledge engineering can play in the process of digital transformation of the wind energy sector. It presents the main concepts underpinning Knowledge-Based Systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to domain experts. A systematic analysis of the current state-of-the-art on knowledge engineering in the wind energy domain is performed, with available tools put into perspective by establishing the main domain actors and their needs and identifying key problematic areas. Finally, guidelines for further development and improvement are provided.