Πλοήγηση ανά Συγγραφέας "Vasileiou, Ioannis"

Τώρα δείχνει 1 - 2 of 2
  • Μικρογραφία εικόνας
    Τεκμήριο
    Coverage-Based Summaries for RDF KBs.
    (ΕΛ.ΜΕ.ΠΑ., Σχολή Μηχανικών (ΣΜΗΧ), ΠΜΣ Πληροφορική και Πολυμέσα, 2021-04-01) Vasileiou, Ioannis; Βασιλείου, Ιωάννης
    As more and more data become available as linked data, the need for efficient and effective methods for their exploration becomes apparent. Semantic summaries try to extract meaning from data, while reducing its size. State of the art structural semantic summaries, focus primarily on the graph structure of the data, trying to maximize the summary’s utility for query answering, i.e. the query coverage. In this thesis, we present four algorithms, trying to maximize the aforementioned query coverage using ideas borrowed from result diversification. The key idea among all algorithms is, instead of focusing only to the “central” nodes, to push node selection also to the perimeter of the graph. Our experiments show the potential of our algorithms and demonstrate the considerable advantages gained for answering larger fragments of user queries.
  • Μικρογραφία εικόνας
    Τεκμήριο
    Workload based summaries for knowledge graphs
    (ΕΛΜΕΠΑ, Σχολή Μηχανικών (ΣΜΗΧ), Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, 2024-10-16) Vasileiou, Ioannis; Βασιλείου, Ιωάννης; Papadakis, Nikolaos; Παπαδάκης, Νικόλαος
    This dissertation delves into the advancements in semantic summarization and user centric exploration of Knowledge Graphs (KGs), driven by the rapid expansion of interconnected data. Semantic summaries have become critical tools for distilling vast datasets into manageable sizes, optimizing query answering, indexing, and visualization. Recent developments in structural semantic summaries have focused on extracting central nodes from the semantic graph, exploiting several graph centrality measures, then linking them and presenting them as summaries. Those summaries can then be used among others to optimize query answering as the size of the graph is drastically reduced. However, as the semantic graphs are heterogeneous, using variations of centrality measures for selecting parts of the graph to be used as a summary, generates summaries with limited benefits for query answering. Leveraging user query workloads has the potential to offer tangible benefits to this direction, as they can offer unique insights on trends and user interests as they evolve over time. To this direction, this dissertation starts exploring workload-based summaries by selecting nodes based on their frequency in query workloads. This drastically improves the usefulness of the result summaries in terms of query coverage. Then it explores how utilizing query logs and Large Language Models (LLMs) can lead to the automatically generation of FAQs, enabling users to rapidly understand the contents of an entire KGjust visiting a set of questions and their answers in textual format. In parallel, we explore shifts in user interests over time using query logs and language models, facilitating users' visualization and understanding of these evolving interests. Then we focus on how to construct personalized summaries that adapt to individual user preferences. We again exploit query logs selecting queries similar to the interests of the user and generate summaries maximizing coverage for user queries, dominating all baselines and competitors. Finally we focus on how workload-based summaries can be used for the generation of compact structures that can be used as a caching mechanism to rapidly provide a first answer to user queries before answering their queries in full. We demonstrate that such summaries are both practical, as they can be trivially constructed and retained in main memory, and also of high benefit as they can significantly optimize the time required for the first results of user queries. By integrating these innovative approaches, this dissertation aims to advance the field of semantic summarization and user-centric KG exploration, fostering more effective and efficient data exploration in increasingly interconnected environments.