Semantic Retrieval of Trademarks Based on Conceptual Similarity

Abstract

Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. This paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky’s theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1400 disputed cases and a database of 378 943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the ${R}$ -precision score, which is commonly used in information retrieval and human judgment/collective human opinion, which is used in human–machine systems.