ABSTRACT. Bipolar disorder is a condition characterised by episodes of extreme mood fluctuation. Effective treatment typically includes psychoeducation, which aims to equip patients with the knowledge needed to make informed decisions that can influence the course of their illness. Key to this process is the ability to monitor the patient's behaviour (both by patients themselves and involved healthcare specialists) in order to recognise early signs of future episodes. To support this monitoring task, we propose a framework grounded in medical ontologies and metric interval temporal logic (MITL). The framework enables real-time monitoring of patient behaviour and the identification of emerging episodes based on behavioral patterns and established clinical guidelines. This paper outlines the formal foundations of the framework and describes future steps towards its adoption in practice.

ABSTRACT. In this work, we consider binary temporal relations, i.e. sets of pairs of time points that capture how events are temporally connected. Such relations arise naturally in applications like as multi-step journey planning, uncertain cause-effect analysis, or disease propagation modelling. Recently, a graph query language called Temporal Regular Path Queries (TRPQs) was introduced in [1] that operates on such relations, by navigating through temporal graphs via regular path expressions extended with a temporal navigation operator. An open question was how to represent TRPQ answers compactly over dense time or time domains of varying granularity, and how compactness can be preserved throughout query evaluation. We addressed these issues in [2], in which we proposed and studied four compact representations of answers to TRPQ. The representations range from two simpler ones that aggregate answers along a single temporal dimension (either departure times or travel distances), to two more complex ones that aggregate results over both dimensions. This line of work opened several questions that remain unaddressed, which we discuss in this paper. Precisely, we discuss: (i) how to abstract TRPQs to graphs annotated with arbitrary Kleene algebras, in order to benefit from results on semiring-based frameworks for relational and path queries; (ii) the relationship between TRPQs and temporal query languages based on Allen’s interval algebra; (iii) alternative representations and visualization strategies for TRPQ answer sets; and (iv) implementation techniques for the two more complex representations, for which coalescing is intractable and cannot be implemented in standard SQL, as identified in our companion implementation work [3].

ABSTRACT. The analysis of gender stereotypes in Brazilian sexual violence judicial proceedings represents a critical yet computationally underexplored challenge given strict digital sovereignty requirements and the heterogeneous nature of Brazilian judicial documents. Existing neuro-symbolic frameworks for Knowledge Graph (KG) and Large Language Model (LLM) integration, while effective in controlled settings, have not yet been designed to address the constraints inherent to this domain. This work introduces FORGE (Framework for Ontology-grounded Retrieval and Graph Extraction), an on-premise framework applied to structured information extraction from sexual violence proceedings. FORGE combines (i) locally-hosted Vision-Language Models and Large Language Models for OCR and structured information extraction over heterogeneous scanned documents, (ii) ontology-grounded retrieval and extraction anchored in an established gender-bias ontology derived from Brazilian sociological research, and (iii) a Virtual Knowledge Graph layer, which exposes a relational backend through the ontology. Preliminary execution over 10 manually validated proceedings yielded a VKG of 926 triples, preserving the fine-grained taxonomy of the ontology, demonstrating the viability of an end-to-end neuro-symbolic framework that operates without dependence on proprietary generative models.

ABSTRACT. We investigate the combination of Knowledge Graphs (KGs) and Large Language Models (LLMs) as a means of grounding model responses in verified organizational data and reducing hallucinations. A domain ontology covering people, skills, positions, and projects was defined and instantiated in a Neo4j graph database. A GraphRAG pipeline was built on top of this graph: natural language questions are translated into Cypher queries, executed against the KG, and the retrieved structured data is supplied as context for LLM response generation. Three architectures were evaluated on a manually validated gold-standard corpus of 95 question–query–response triples: GPT-4 standalone, a locally-run quantized model fine-tuned for Cypher generation (text-to-cypherGemma-3-27B, referred to as HF 27B), and a hybrid combining both. HF 27B achieved a perfect 100% Cypher execution rate against 97.9% for GPT-4, but only 43.2% factual consistency in the generated answers. GPT-4 reached 68.4% consistency and the hybrid 52.6%. The gap between the hybrid and GPT-4 standalone reveals that a semantically incorrect Cypher query cannot be rescued by a better text generator, making query correctness the binding constraint of the whole pipeline. Results support GraphRAG as a viable and practical strategy for domains requiring factual traceability, such as strategic personnel planning and legal research.

ABSTRACT. Virtual Knowledge Graphs (VKGs) expose relational data through domain ontologies and declarative R2RML mappings, but constructing such mappings remains labor-intensive and requires expertise in databases, ontology engineering, and mapping languages. Existing LLM-based approaches often rely on context-overloaded prompts and provide limited support for grounding decisions across iterations. This paper proposes AgenticVKG, a vision for a memory-driven agentic framework for VKG mapping construction. The architecture combines an embedding-based Retriever, an LLM-based Matcher, and a persistent Memory organizing database context, mapping patterns, past decisions, and agent reports. Through iterative mapping rounds, AgenticVKG aims to ground each decision in a shared, cumulative context, providing a basis for more consistent and traceable R2RML mapping construction.