The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far.In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning.We present SEON, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time.For validation, we discuss three different approaches, which are backed by SEON and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization. Abstract The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far. In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning. We present Seon, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time. For validation, we discuss three different approaches, which are backed by Seon and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization.