Konstantina Chrysafiadi

Wikidata provides extensive coverage of tourism-related Points of Interest (POIs), yet its heterogeneous type system and uneven metadata limit its direct use in smart tourism applications. This paper presents an end-to-end pipeline that transforms Wikidata POIs into a compact and interpretable tourism-oriented representation supporting multi-category assignments. We collect POIs from six countries—Greece, Italy, Spain, Norway, Sweden, and Denmark—and construct a dataset that integrates core identifiers with textual descriptions, type information, heritage indicators, geographic coordinates, and Wikipedia sitelinks. We introduce an eight-category tourism taxonomy capturing key themes, including cultural venues, archaeological and historic sites, monuments, fortifications, religious sites, protected areas, natural features, and coastal or water locations. As a reproducible baseline, category likelihoods are estimated using sentence embeddings and similarity to category anchor descriptions, producing a probability vector for each POI. Building on this baseline, we propose a fuzzy inference layer that integrates embedding-based probabilities with structured Wikidata signals to generate interpretable membership degrees across categories and enable principled multi-category classification. This fusion is particularly valuable for smart tourism applications, as it supports robust faceted exploration and personalized recommendations (e.g., “historic + coastal”), while providing evidence-based explanations that enhance user trust and facilitate curator oversight when POI metadata is sparse or ambiguous. The resulting pipeline produces ranked POI catalogs by country and category, country-level tourism profiles, and diagnostic views for examining uncertain cases. The approach is fully reproducible and readily adaptable to other geographic regions or domain taxonomies.

This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and validatable cross-cultural lyric analysis. The core component, Sequential Language Model Integration (SLMI), enhances the interpretability and reliability of transformer-based LLMs by addressing explainability and validation challenges through Retrieval-Augmented Generation (RAG), hybrid search, and rule-based evaluation. An important feature of LYRICEL is its use of KG visualizations, which serve as dynamic links to improve interpretability and validatability by structuring data relationships and sources. These visualizations are central to advancements in four areas: KG representation learning, knowledge acquisition, temporal KGs, and knowledge-aware applications. Tested on Greek folk music with models like GPT-4o and BERT, LYRICEL’s trustworthiness is assessed using the VIRTSI model, which quantifies cognitive trust in human-computer interactions. The framework shows strong potential for cross-cultural applications, particularly in languages such as Modern Greek which encompasses a rich cultural heritage spanning centuries of history and traditions resulting in a complex study. The outcomes of GPT-enabled LYRICEL are compared to ChatGPT alone and show a significant improvement in the reliability and efficiency of interactions that can reach a global audience, enhancing the accessibility and understanding of diverse cultural heritages.

As Artificial Intelligence becomes increasingly integrated into educational games, conforming with the General Data Protection Regulation (GDPR)—a legal framework governing data protection and privacy in the European Union—remains an important yet complex challenge, particularly when minors are involved. Users are required to provide consent multiple times, often unexpectedly, at different game levels. This process is further complicated by the varying durations for which consent remains valid. As a result, users—especially minors—may become confused about the consent they have given. Additional concerns arise when the educational game is AI-equipped. If AI is not involved, no new data is generated. However, if AI is present, new data is continuously produced, necessitating ongoing consent. For example, a user may consent to personalisation, which could lead the game to categorise them in unintended ways, such as labelling them a ‘poor student’. This paper explores GDPR challenges in AI-empowered educational games, focusing on user consent, AI-inferred data, and compliance gaps. Intelligent educational games rely on adaptive decision-making algorithms to personalise learning experiences, making them a subset of Intelligent Decision Technologies. Our research is based on a fuzzy-based educational game developed as a testbed for studying GDPR compliance in AI-driven decision-making. The findings provide insights into ethical AI governance, dynamic consent management, and the intersection of regulatory compliance with adaptive, data-driven decision systems in intelligent educational technologies. Based on our research, not all personal data exist from the beginning and upon original consent granting, as personal data are also generated throughout the process.

In the era of smart tourism, providing seamless and personalized experiences has become significant for enhancing user satisfaction and engagement. This paper presents a novel fuzzy logic-based application system designed to enhance personalization in smart tourism. The proposed system integrates real-time user data and delivers customized services to each particular user. In particular, the proposed system incorporates a recommendation mechanism that combines TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with fuzzy logic to assess multiple criteria and user preferences and provide accurate and well-rounded personalized travel destination recommendations. By employing fuzzy logic, the system effectively overcomes challenges associated with uncertainty and subjectivity in user data, enabling precise and adaptable decision-making and ensuring more accurate service recommendations. Through case studies and simulations, the paper evaluates the system’s impact on enhancing user satisfaction and the overall tourism experience. Furthermore, preliminary evaluation results demonstrate the system’s ability to generate meaningful and seamless personalized recommendations that enhance the provided tourism services. This work contributes to the growing field of smart tourism by offering a scalable and user-centric solution. The scalability of the system is ensured through its efficient handling of multidimensional data, adaptability to diverse user profiles, and extendability to various tourism applications, including destination ranking, activity recommendations, and hotel selection. Additionally, its integration potential with existing travel platforms highlights its applicability in real-world scenarios, making it a robust tool for enhancing smart-tourism experiences.

Recent advancements in intelligent tutoring systems (ITS) driven by artificial intelligence (AI) have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners’ backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support the effective attainment of learning objectives has become a critical challenge. This paper introduces personalized fuzzy logic strategies for intelligent programming tutoring (PerFuSIT), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT’s decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners’ strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction.