Exploring the Landscape of Tourism Knowledge Graphs: A Systematic Literature Review

Knowledge graphs (KGs) are important tools for organizing and leveraging machine-readable knowledge across various domains. This paper presents a systematic literature review examining the state of research on tourism knowledge graphs (TKGs). Our review followed Kitchenham guidelines and developed four research questions to accomplish the objectives of the study. We analyzed 35 relevant studies from 151 English-language publications published between 2013 and 2023 to identify trends in TKG construction, application, and evaluation. Our findings revealed that TKGs are predominantly built using graph data models such as RDF and LPG, stored in graph databases such as GraphDB and Neo4J, and extracted from diverse tourism data sources. The main applications include tourism recommender systems, question-answering systems, conversational assistants, and data analytics. European TKG projects prioritize semantic interoperability, while Asian projects focus on performance optimization. The TKG quality is evaluated primarily through metrics of correctness and completeness or through case study applications. However, our review identified several key gaps: limited scope of applications focusing primarily on tourist-centric use cases, lack of comprehensive construction methodologies, insufficient evaluation across multiple quality dimensions, and underutilization of TKGs for sustainable and responsible tourism practices. We propose directions for future research to address these limitations, including developing TKGs to support sustainable tourism practices, integrating TKGs with emerging technologies, expanding applications to benefit a wider range of stakeholders, and enhancing evaluation methodologies. This study contributes to the advancement of intelligent tourism systems by providing a comprehensive overview of the TKG landscape and guiding future research efforts.