Technology has dramatically changed the way criminals conduct their illicit activities. Specifically, the Internet has become a major facilitator of online human sex trafficking. Traffickers are using these technologies to market their victims which presents new challenges for efforts to combat sex trafficking. This study used knowledge management principles and natural language processing methods to develop an improved ontology of online sex trafficking advertisements. The language of these advertisements is constantly evolving; therefore, this study explored the role of a new type of indicator, emoticons, to the ontology of human trafficking indicators. Online classifieds advertisements from three major southern California cities/counties; San Diego, Los Angeles, and Orange County; were retrieved from Backpage.com as the dataset. Six known trafficking indicator categories, their associated keyword counterparts, and this data were used for the development of a new ontology of emoji indicators. The findings from this study indicate that emoticons have become the primary representation of four known trafficking indicators in advertisements, sale of services, underage victim, transient activity, and restricted movement. The key emoji indicators of trafficking for these categories are the rose, rosette, cherry, cherry blossom, growing heart, airplane, and crown emoticons. Alternatively, keywords are still the primary representation of ethnicity and county of origin in advertisements. Adoption of these emoji indicators for these categories is most prevalent for potential victims with East / South Asia origins and Hispanic/Asian/African American racial backgrounds. Additionally, the use of multiple emoji indicators links to a higher likelihood of the use of country of origin and ethnicity emoji indicators. Application of the model to the Backpage.com dataset indicated that four emoji indicators, four keyword indicators, or five indicators using the combined keyword/emoji ontology must be used in an advertisement to warrant a potential investigation. The proposed ontology provides a framework for the development of an automated knowledge management systems for filtering through the noise in online classified sites to identify potential cases of human sex trafficking. This KMS tool provides law enforcement with actionable intelligence in support of their counter human trafficking efforts.