Conventional exhibition management information systems recommend tourist attractions that are close to the place in which an exhibition is held. Some recommended attractions by the location-based recommendation could be meaningless when nothing is related to the exhibition’s topic. Our goal is to recommend attractions that are related to the content presented in the exhibition, which can be coined as content-based recommendation. Even though human exhibition curators can do this, the quality is limited to their manual task and knowledge. We propose an automatic way of discovering attractions relevant to an exhibition of interests. Language resources are incorporated to discover attractions that are more meaningful. Because a typical single machine is unable to deal with such large-scale language resources efficiently, we implemented the algorithm on top of Apache Spark, which is a well-known distributed computing framework. As a user interface prototype, a web-based system is implemented that provides users with a list of relevant attractions when users are browsing exhibition information, available at http://bike.snu.ac.kr/WARP. We carried out a case study based on Korean Tourism Organization Linked Open Data with Korean Wikipedia as a language resource. Experimental results are demonstrated to show the efficiency and effectiveness of the proposed system. The effectiveness was evaluated against well-known exhibitions. It is expected that the proposed approach will contribute to the development of both exhibition and tourist industries by motivating exhibition visitors to become active tourists.