Abstract Converting natural language text into structured, logically coherent knowledge graphs (KGs) enhances the ability to retrieve, organize, and analyze vast amounts of information at scale. This paper introduces Text2AMR2FRED, a text-to-KG pipeline that converts multilingual natural language text into logically coherent, interoperable KGs. Designed to support large-scale information retrieval and knowledge extraction, this pipeline addresses key limitations of existing semantic parsers and machine readers, including issues with logical consistency and interoperability. By adhering to Semantic Web standards, Text2AMR2FRED systematically structures text-based information and enhances it through integration with external knowledge sources, delivering enriched, semantically sound KGs ready for diverse applications. We obtain the output KGs by leveraging Abstract Meaning Representation (AMR) as an intermediate semantic parsing formalism, exploiting the progress achieved by text-to-AMR parsers employing pre-trained language models. We produce a manually validated KG s bank created by transforming a dataset of natural language sentences into KGs using Text2AMR2FRED and applying an intrinsic evaluation method that leverages Open Knowledge Extraction motifs.

The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM reactive capability to address complex problems and interpret deeply contextual real-world meaning. We introduce a method and a tool for creating a multimodal, knowledge-augmented formal representation of meaning that combines the strengths of large language models with structured semantic representations. Our method begins with an image input, utilizing state-of-the-art large language models to generate a natural language description. This description is then transformed into an Meaning Representation (AMR) graph, which is formalized and enriched with logical design patterns, and layered semantics derived from linguistic and factual knowledge bases. The resulting graph is then fed back into the LLM to be extended with implicit knowledge activated by complex heuristic learning, including semantic implicatures, moral values, embodied cognition, and metaphorical representations. By bridging the gap between unstructured language models and formal semantic structures, our method opens new avenues for tackling intricate problems in natural language understanding and reasoning. • Neurosymbolic approach combining Large Language Models (LLMs) and knowledge graphs. • Extended Knowledge Graphs for implicit knowledge in multiple semantic dimensions. • 11 heuristics to enrich formal representations of meaning from multimodal inputs. • Three-tiered evaluation: logical validation, ontology alignment, human assessment.

Providing operational support to clinicians during their daily activities in hospital wards is a challenge for information technologies. In particular, solutions should provide very usable user interfaces, possibly deployed on mobile devices, and should be able to enact and monitor the execution of clinical guidelines. In this paper, we present the preliminary outcomes of the TESTMED project, a small project in which vocal and touch interfaces are being experimented as a viable solution for clinicians' interaction with the system, and a process-aware approach has been undertaken for the (semi-)automation of clinical guidelines.

SUMMARY Nowadays, process‐aware information systems (PAISs) are widely used for the management of ‘administrative’ processes characterized by clear and well‐defined structures. Besides those scenarios, PAISs can be used also in mobile and pervasive scenarios, where process participants can be only equipped with smart devices, such as personal digital assistants. None of existing PAISs can be entirely deployed on smart devices, making unfeasible its usage in highly mobile scenarios. This paper presents ROME4EU, a mobile PAIS developed for being applied to the coordination of emergency operators, and an extensive validation of the system, both in term of performances and usability/acceptability by the users. Copyright © 2011 John Wiley & Sons, Ltd.

Process Management Systems (PMSs, a.k.a. Workflow Management Systems - WfMSs) are currently more and more used as a supporting tool to coordinate the enactment of processes. In real world scenarios, the environment may change in unexpected ways so as to prevent a process from being successfully c