Background: Chemotherapy, a central cancer treatment, employs antineoplastic drugs to hinder cancer cell replication by disrupting DNA synthesis or mitosis. Chemotherapies follow complex protocols composed of cycles of treatment where antineoplastic and adjuvant drugs prescribed at different doses and times. Various protocols exist, with either small or large and numerous variations to others, making it hard to compare chemotherapies to each other, comparing their differential outcomes, and in the end choosing the most adapted one for a particular patient. Method: We propose ChemoKG, a knowledge graph for chemotherapy protocols that encompasses first administration programs such as drugs, dosages, treatment durations, and second drug properties and classes imported from ChEBI, DrugBank and the ATC classification. Three resources on drugs provide complementary hierarchies and chemical properties that help to better identify similar chemotherapy protocols. To this aim, we tested on ChemoKG a novel graph embedding method employing graph neural networks (GNNs) to compare nodes in the graph that represent protocols. Unlike previous approaches that focus on triple-based embeddings, the proposed method captures subgraph structures inherited from the aggregation scheme in GNNs. Results: The resulting knowledge graph encompasses 329,164 triples with 99,901 entities and 75 predicates including 1,358 chemotherapy protocols and 226 anti-cancer drugs. We performed a cluster analysis of protocol embeddings learned on ChemoKG, to propose groups of similar protocols. This will contribute in facilitating the comparison of chemotherapy themselves, and by extension to their potential effectiveness. Additionally, it should aid in analyzing gaps between commonly accepted protocols and their real-world implementation.