Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.

In human resource management of large organisations, finding the best candidate for a job description requires an extensive examination of a large number of resume profiles. Even with the advent of Deep Information Retrieval and the supported semantic similarity search, identification of relevant skills within profiles requires thorough investigation over several aspects, including educational background, professional experience, achievements, etc. However, these techniques are based on the existence of domain-specific, human-annotated datasets, a laborious task that portrays high cost and a slow labeling progress. In this paper, we propose Resume2Skill-SE, an end-to-end architecture for interpretable skill-based talent matching. The solution consists of two components. The first module uses an unsupervised approach for skills extraction based on state-of-the-art text embeddings and efficient semantic similarity search. The second module creates a profile-skills bipartite graph and uses a proposed ranking formula for similar resume profiles, minimising the effect of potential errors from the skills extraction module. The optimal ranking formula was identified through an intuitive and automated evaluation method for getting relevance scores. The proposed technique delivers promising results while also including an interpretability layer by showing the common skills of a pair of resume profiles.

The profound digitization of public administration over recent decades has not eliminated information exchange via paper or electronic documents and certificates. We argue that a paradigm shift from document-centric to data-centric public service provision is needed and is feasible today with the exploitation of emerging technologies. We explore frameworks, architectures, benefits, and challenges in transforming document-centric administration processes into integrated, granular data exchange. A conceptual architecture for public service provision is proposed to extract preconditions from legislation, map the needed evidence to the service requirements, standardize evidence types, and integrate authoritative data sources. While promoting efficiency, privacy, and innovation, this shift faces technical and organizational challenges as the analysis of the “National Registry of Administrative Public Services” in Greece reveals. Further research on aligning policies, upholding trust, and coordinating institutional processes is warranted.

Public service provision is still limited to document-centric procedures that require citizens to submit data and information needed for the execution of a service via documents. This, amongst others, is time-consuming, error-prone and hinders progress towards data-centricity. This study proposes a data-centric framework for a public service recommender system that combines knowledge graphs (KGs) and large language models (LLMs) in a neuro-symbolic AI architecture. The framework expresses public service preconditions as machine-readable rules based on data standards and provides dynamic recommendations for public services based on citizens’ profiles through automated reasoning. LLMs are utilized to extract preconditions from unstructured textual regulations and create RDF-based evidence models, while KGs provide validation of preconditions through SHACL rules and explainable reasoning towards semantic interoperability. A prototype use case on students applying for housing allowance showcases the feasibility of the proposed framework. The analysis indicates that combining KGs with LLMs for identifying relevant public services for different citizens’ profiles can improve the quality of public services and reduce administrative burdens. This work contributes and promotes the proactive “No-Stop Government” model, where services are recommended to users without explicit requests. The findings highlight the promising potential of employing neuro-symbolic AI to transform e-government processes, while also addressing challenges related to legal complexity, privacy and data fragmentation for large-scale adoption.