With the advancements in artifical intelligence and semantic web technologies, structures to organize and model data, such as semantic networks, knowledge graphs, and ontologies, have become widespread. A well-knwon example is the Google Knowledge Graph, but there are many other projects, such as ConceptNet, Freebase, YAGO, OpenCyc and DBPedia, that are openly available, and, as the result of knowledge extraction and data mining techniques, contain vast amounts of organized common-sense and general world knowledge on different topics. However, the quality of the knowledge they contain varies, and irrelevant and possibly flawed contents coexist with meaningful knowledge, making them harder to grasp. They also lack a formal logic-based semantics, as enjoyed by ontologies, and therefore they cannot be readily exploited for tasks that require correct and non-trivial reasoning. We claim that the vast work invested into knowledge graphs can be leveraged to build, smallto middle-sized, topic-specific ontologies of every-day domains. We describe a simple, yet effective method that extracts thematically relevant knowledge from a knowledge-graph and, in a stepwise procedure, provides suggestions that help to interactively build a Description Logic (DL) topic ontology with moderate efforts. We present an implementation of our method in the semi-automatic CN2TopicOnto tool that systematically queries ConceptNet and suggests suitable concept names to the user, who can use them to create axioms in a simple command-line interface. The supported axioms range from plain inclusions between classes, to complex ontological axioms in expressive DLs. With our proof-of-concept prototype it is possible to build ontologies on different everyday topics in reasonable time. To demonstrate its usefulness we describe some illustrative ontologies on the topics animals, fruits, vehicles and natural disasters that were created with CN2TopicOnto.