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 way towards sustainable development is paved through the commitment to the 17 Sustainable Development Goals (SDGs), which encompass a wide range of global challenges. The successful progress of these goals depends on the identification and understanding of their interconnected nature. A plethora of data is made available for tracking targets related to the SDGs at country, regional and urban levels. However, various challenges are identified to semantically align and homogeneously represent such data to improve their interoperability, comparability and analysis. In the current work, we provide an innovative solution for analyzing SDG-oriented data based on the development of a Knowledge Graph that provides access to semantically aligned data for the SDGs. We consider Knowledge Graphs as a suitable technology for the representation of data related to the interlinkages among SDGs, since they provide a structured representation of knowledge that incorporates entities, relationships and attributes, organized in a graph format. We examine the interlinkages among indicators of the same SDG, as well as across indicators of the various SDGs. Such interlinkages are further evaluated as synergies or trade-offs. Our analysis is applied in country and regional levels, considering various constraints in terms of data quality and availability. In total 476 synergies are identified at the national level among the SDGs, compared to 140 trade-offs. The SDGs that mostly participate in the synergies are SDGs 17, 10, 9 and 8, while SDGs 7 and 16 participate in most of the trade-offs. At the regional level, SDGs 8, 4 and 9 are more active in terms of interlinkages.

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.

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 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).