In this paper, we describe an extension of the ontology of core data mining entities (OntoDM-core) that will improve the semantic representation of machine learning and data mining algorithms. The OntoDM-core acknowledges the multi-faceted aspect of algorithms and accordingly provides entities, such as algorithm specification, algorithm implementation, and algorithm execution. We build upon this representation and include a more detailed representation of algorithms, including terms such as hyperparameter, optimization problem, complexity function, etc. Furthermore, we discuss the potential applications of the ontology. It can be used as a backbone of a repository and knowledge base for storing semantic annotations of algorithms and for assisting algorithm developers and domain experts with the task of manual semantic annotation. Ultimately, the corpus of manually annotated algorithms using the ontology vocabulary will serve as a foundation for automating the process of semantic annotation of algorithms from text using natural language processing techniques.