We outline the use of the tool FAMER to address the schema and entity matching tasks for the DI2KG 2019 challenge. FAMER supports both the static and incremental matching and clustering of entities from multiple sources. To alleviate entity matching, we first identify matching properties in the provided datasets based on the similarity of property names and instance values. This approach utilizes the given training data to derive property matches from entity matches. For entity matching, we consider multiple configurations to determine entity similarities with the optional use of word embeddings.