Eleni Fotopoulou

The European Green Deal (EGD) is the European Union’s comprehensive growth strategy for achieving climate neutrality by 2050. It comprises 17 interrelated policy documents, spanning sectors from energy and transport to biodiversity and sustainable finance. Despite their collective importance, these documents are characterized by significant heterogeneity in structure, terminology, and scope, making it challenging for non-technical stakeholders to navigate, cross-reference, extract, and validate information across their corpus as a whole. Considering the limitations of Natural Language Processing (NLP) approaches targeting the accessibility of policy documents and the lack of prior work explicitly focusing on the EGD and sustainability, we introduce a graph retrieval-augmented generation (GraphRAG) pipeline for natural language question answering (QA) over the EGD corpus. Our contributions include the conceptualization of a generalizable entity type set for policy documents for the EGD and its representation in the form of a knowledge graph, the development of two novel graph-based retrieval strategies that exploit the pre-computed structural properties of the knowledge graph, and the release of a specialized evaluation dataset, built on persona profiles matching real-world user profiles. The implementation and evaluation of the proposed approach are detailed, highlighting its effectiveness for the analysis of policy documents for the EGD against other GraphRAG baselines.

A paradigm shift is underway in 6G from the design of reactive systems to autonomous and adaptive solutions that take advantage of agentic Artificial Intelligence (AI). Agentic AI solutions emerge to inject distributed intelligence into 6G management and orchestration, where multiple agents can plan steps and collaborate to achieve common goals. In the current article, we detail an Agentic AI approach for managing the intent for distributed applications deployed over 6G infrastructure. Distributed applications are provided on computing resources that span from the radio access to the edge to the core part of a 6G-enabled computing continuum. The proposed approach leverages Knowledge Graph and Generative AI technologies, enabling agents to reason over explicit relationships and dynamically generate explainable and reliable plans. A prototype implementation is detailed, accompanied by evaluation results that highlight the efficiency of the proposed approach to improve automation and efficiency on the intent lifecycle management.

The positive impact of Social and Emotional Learning (SEL) activities in schools is widely recognized in various sectors, including mental health, social relationships, and academic performance of students. This impact can be efficiently realized and catalyzed based on the development of tools that help teachers to effectively and consistently apply SEL in classrooms. Such tools can leverage emerging Artificial Intelligence (AI) technologies, including Generative AI (GenAI) and Agentic AI, and Knowledge Graphs to enable their ease of adoption and use by teachers and provide accurate and context-aware guidance. In this perspective, this manuscript presents an Agentic Graph Retrieval-Augmented Generation (RAG) approach to support educators to apply methodologies for SEL. The approach is materialized through an agent, called MySELAgent, which synthesizes information and provides recommendations around SEL, taking advantage of GenAI and following an Agentic AI approach. MySELAgent is interoperable with third-party psychometric tools, enabling assessment and monitoring of the social and emotional competencies of students. It is able to suggest evidence-based targeted SEL activities and create holistic SEL intervention programs in the form of learning pathways, which are based on teachers’ preferences and students’ emotional needs. A detailed evaluation of the implemented techniques is presented, analyzing the performance and effectiveness of MySELAgent on retrieving information and creating accurate responses based on the use of different Large Language Models (LLMs).

Several data and knowledge management services have recently been produced to address the challenges related to climate change. However, many of the proposed solutions are not made openly available or consider their operation over a set of assumptions in terms of data usage, limiting the potential for their adoption by a wider community. Furthermore, most of the existing work is operational in the form of software or data silos, without giving high importance to interoperability and extensibility features and with a high level of obscurity in the supported mechanisms. This article details the Knowledge Management Framework developed in the ARSINOE project, which is an EU-funded project aimed at creating climate resilient-regions through systemic solutions and innovations. The ARSINOE Knowledge Management Framework leverages modern technologies such as Knowledge Graphs and Digital Twins. It includes a Data Hub to host and make available heterogeneous environmental and climate data, a Knowledge Graph that tracks information related to the Sustainable Development Goals and enables the development of participatory socio-ecological modeling and analysis, interfaces for the development of Digital Twins that onboard socio-ecological models, as well as visualization and analysis services. The ARSINOE Knowledge Management Framework is open, modular and extensible by design, while it addresses data quality assessment and management challenges, strengthening its adoption, comparability, and replicability in different locations. The general framework is presented, focusing on the functionality of each component, their interplay, and its applicability in specific case studies throughout Europe.

The integration of Knowledge Graphs (KGs) with Question Answering (QA) systems is transforming the landscape of Artificial Intelligence (AI). Through the combination of these technologies, novel features can be provided for the translation of questions in natural language into database queries. Even if a lot of work is emerging in this domain, this is not the case when we refer to translation of text to Cypher queries, where Cypher is one of the dominant query languages used for the development of KGs (e.g., based on the Neo4j technology). In this context, this paper provides a robust and efficient framework to systematically assess the efficiency of Large Language Models (LLMs) to support Text-to-Cypher conversion, focusing on the evaluation of open-source LLMs. The framework utilizes metrics and validators offered by an open-source software library that we developed, called CyVer. This study also assesses the impact of different schema representations of the KG on schema-aware query generation and the performance of LLMs on questions of different complexity requiring a depth of reasoning on the KG. A case study is described based on the application of the detailed framework in a KG with a large and complex schema that hosts data to track information related to the Sustainable Development Goals (SDGs). The experimental results demonstrate the effectiveness of the proposed framework, highlight the importance of the size of open-source models in the semantic comprehension of questions and the generation of valid Cypher queries, and stress the challenge for the generation of accurate queries in the case of questions requiring complex Cypher logic.

The application of Social and Emotional Learning (SEL) methodologies within classrooms has been widely adopted and examined, highlighting its positive impact on the academic, emotional, and social sectors at both the individual and classroom levels. Through this wide applicability, a need is identified to focus more on the development of SEL approaches that support the integration of the various parts of a SEL methodology (e.g., planning, implementation, monitoring, and assessment). Such approaches should not underestimate the value of the SEL assessment. It is necessary to engage both the developers and practitioners of SEL methodologies and provide tools to assist teachers to better interpret the collected data and results and plan targeted SEL activities. Motivated by such needs, we detail the EduCardia SEL methodology that focuses on the provision of a set of guidelines and tools to assess and improve the social and emotional competencies of students in primary and secondary educational levels, by taking advantage of Information and Communication Technologies (ICT). The EduCardia SEL methodology considers the integration of the various parts of a SEL methodological approach and supports continuous assessment processes, while offering user-friendly interfaces to interpret the produced results. Evaluation results are provided based on the application of the EduCardia SEL methodology in a large set of K12 schools across Greece for a short-term period, considering three different age groups. It is shown that the targeted competencies are improved, while insights are provided by teachers for the applicability and efficiency of the proposed approach.

Climate change vulnerability assessment (CCVA) is crucial to understand how different regions, communities, and ecosystems are susceptible to the impacts of climate change. Through the identification of vulnerabilities, policymakers can prioritize adaptation efforts, allocate resources effectively, and develop risk mitigation strategies. Social and environmental scientists can design solutions for the development of climate-resilient regions. Nowadays, various approaches have been made available for CCVA, leading to increased knowledge along with the identification of challenges and pitfalls. Participatory approaches are required for the proper selection of vulnerability indicators and the interpretation of the assessment results, accompanied by the development of quality data assessment processes to reduce inconsistencies and uncertainties in the provided data. Openness and interoperability of data and processes have to be promoted to enable the extensibility, reproducibility and comparability of the produced results. In the current work, we develop a framework to enable open and interoperable CCVA, taking advantage of a knowledge management infrastructure and specifically a Knowledge Graph that tracks information for various indicators around the Sustainable Development Goals. We follow an indicator-based vulnerability assessment approach for various temporal and spatial scales, while considering hazard-independent and hazard-dependent analysis. Indicative analysis results are provided in national, regional and local scale.