The paper "Ontologies Are Us: A Unified Model of Social Networks and Semantics" by Peter Mika explores the integration of social dimensions into traditional ontologies, leading to a tripartite model that includes actors, concepts, and instances. This model is used to demonstrate how community-based semantics can emerge through graph transformations. The author addresses the issue of ontology drift by proposing *emergent semantics*, where individual interactions of rational agents lead to global effects that can be observed as semantics. The paper introduces the Actor-Concept-Instance model, which represents semantic-social networks as a tripartite graph, extending the traditional bipartite model of ontologies. The author illustrates the application of this model through two case studies: an analysis of a large-scale folksonomy system (del.icio.us) and a method for extracting community-based ontologies from web pages. The first case study shows how emerging semantics can be observed in the folksonomy system, while the second case study demonstrates the extraction of ontologies from web pages based on community relationships. The evaluation of the extracted ontologies is conducted through a survey of researchers in the Semantic Web domain, confirming that the community-based ontology (O_ac) is more accurate and relevant to the community's conceptualizations. The paper concludes by emphasizing the importance of incorporating social context into the representation of ontologies, highlighting the potential benefits of emergent semantics in complementing established ontologies and better capturing the associative nature of community-based knowledge.The paper "Ontologies Are Us: A Unified Model of Social Networks and Semantics" by Peter Mika explores the integration of social dimensions into traditional ontologies, leading to a tripartite model that includes actors, concepts, and instances. This model is used to demonstrate how community-based semantics can emerge through graph transformations. The author addresses the issue of ontology drift by proposing *emergent semantics*, where individual interactions of rational agents lead to global effects that can be observed as semantics. The paper introduces the Actor-Concept-Instance model, which represents semantic-social networks as a tripartite graph, extending the traditional bipartite model of ontologies. The author illustrates the application of this model through two case studies: an analysis of a large-scale folksonomy system (del.icio.us) and a method for extracting community-based ontologies from web pages. The first case study shows how emerging semantics can be observed in the folksonomy system, while the second case study demonstrates the extraction of ontologies from web pages based on community relationships. The evaluation of the extracted ontologies is conducted through a survey of researchers in the Semantic Web domain, confirming that the community-based ontology (O_ac) is more accurate and relevant to the community's conceptualizations. The paper concludes by emphasizing the importance of incorporating social context into the representation of ontologies, highlighting the potential benefits of emergent semantics in complementing established ontologies and better capturing the associative nature of community-based knowledge.