While semantic technologies and the use of ontologies is one of the proposed solutions to the heterogeneity problem in database systems, the independent generation of ontologies leaves us with another heterogeneity problem. We need to match ontologies to allow active co-operation of different systems. This matching can often not be done by human effort alone because of the size and complexity of the ontologies. Thus automatic ontology matching systems are indispensable. We evaluate the usefulness of embeddings, more specifically ontology embeddings both for analytical tasks and for ontology alignment tasks. We further propose OWL2Vec as a framework to create such embeddings. The embeddings are created by projecting the ontology to an RDF-graph, conducting a series of walks on the graph and collect them in a walks document. Natural language embeddings systems such as word2vec are used to train the embeddings. To leverage ontology matching, we propose to create joint embeddings in the same vector space for two (or more ontologies). The user must then provide the system with anchors. The results are promising for analytical tasks such as clustering and partitioning of the ontology into related concepts. We demonstrate that by using the embeddings and the provided anchors, it is possible to find new mappings between two ontologies. We also demonstrate that the structural similarities provided by the embeddings can give ontology matching systems an additional similarity score that could help such systems to decide if a potential mapping is valid.