Dissemination

Title:Logic Augmented Generation

Author:Aldo Gangemi, Andrea Giovanni Nuzzolese

Publication:Journal of Web Semantics, 85:100859, 2025

Date:01.05.2025

Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) may overcome those limitations, making them suitable in open-ended tasks and unstructured environments. Nevertheless, LLMs are hardly interpretable and often unreliable. To take the best out of LLMs and SKGs, we envision Logic Augmented Generation (LAG) to combine the benefits of the two worlds. LAG uses LLMs as Reactive Continuous Knowledge Graphs that can generate potentially infinite relations and tacit knowledge on-demand. LAG uses SKGs to inject a discrete heuristic dimension with clear logical and factual boundaries. We exemplify LAG in two tasks of collective intelligence, i.e., medical diagnostics and climate projections. Understanding the properties and limitations of LAG, which are still mostly unknown, is of utmost importance for enabling a variety of tasks involving tacit knowledge in order to provide interpretable and effective results.

Open access

Title:Ontology Generation Using Large Language Models

Author:Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä, Sara Zuppiroli, Miguel Ceriani, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese

Publication:The Semantic Web. ESWC 2025. Lecture Notes in Computer Science, vol 15718. Springer, Cham.

Date:01.06.2025

The ontology engineering process is complex, time-consuming, and error-prone, even for experienced ontology engineers. In this work, we investigate the potential of Large Language Models (LLMs) to provide effective OWL ontology drafts directly from ontological requirements described using user stories and competency questions. Our main contribution is the presentation and evaluation of two new prompting techniques for automated ontology development: Memoryless CQbyCQ and Ontogenia. We also emphasize the importance of three structural criteria for ontology assessment, alongside expert qualitative evaluation, highlighting the need for a multi-dimensional evaluation in order to capture the quality and usability of the generated ontologies. Our experiments, conducted on a benchmark dataset of ten ontologies with 100 distinct Competency Questions (CQs) and 29 different user stories, compare the performance of three LLMs using the two prompting techniques. The results demonstrate improvements over the current state-of-the-art in LLM-supported ontology engineering. More specifically, the model OpenAI o1-preview with Ontogenia produces ontologies of sufficient quality to meet the requirements of ontology engineers, significantly outperforming novice ontology engineers in modelling ability. However, we still note some common mistakes and variability of result quality, which is important to take into account when using LLMs for ontology authoring support. We discuss these limitations and propose directions for future research.

Open access

Title:Hybrid Collective Intelligence for Decision Support in Complex Open-Ended Domains

Author:Vito Trianni, Andrea Giovanni Nuzzolese, Jaron Porciello, Ralf H. J. M. Kurvers, Stefan M. Herzog, Gioele Barabucci, Aleksandra Berditchevskaia, Fai Fung

Publication:Volume 368 of Frontiers in Artificial Intelligence and Applications. HHAI 2023: Augmenting Human Intellect, pages 124-137, IOS Press

Date:26.06.2023

Human knowledge is growing exponentially, providing huge and sometimes contrasting evidence to support decision making in the realm of complex problems. To fight knowledge fragmentation, collective intelligence leverages groups of experts (possibly from diverse domains) that jointly provide solutions. However, to promote beneficial outcomes and avoid herding, it is necessary to (i) elicit diverse responses and (ii) suitably aggregate them in a collective solution. To this end, AI can help with dealing with large knowledge bases, as well as with reasoning on expert-provided knowledge to support decision-making. A hybrid human-artificial collective intelligence can leverage the complementarity of expert knowledge and machine processing to deal with complex problems. We discuss how such a hybrid human-artificial collective intelligence can be deployed to support decision processes, and we present case studies in two different domains: general medical diagnostics and climate change adaptation management.

Open access

Title:Automating hybrid collective intelligence in open-ended medical diagnostics

Author:Ralf H. J. M. Kurvers, Andrea Giovanni Nuzzolese, Alessandro Russo, Gioele Barabucci, Stefan M. Herzog, and Vito Trianni

Publication:PNAS 120(34):e2221473120

Date:28.08.2023

In the United States, an estimated 250,000 people die annually from preventable medical errors, many of which originate during the diagnostic process. A powerful approach to increase diagnostic accuracy is to combine the diagnoses of multiple diagnosticians. However, we lack methods to aggregate independent diagnoses in general medical diagnostics. Using knowledge engineering methods, we introduce a fully automated solution to this problem. We tested our solution on 1,333 medical cases, each of which was independently diagnosed by ten diagnosticians. Our solution substantially increases diagnostic accuracy: Single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. These results demonstrate that collective intelligence can reduce diagnostic errors, promoting health services and trust in the global medical community.

Open access

Title:D1.1 — IPR Management Plan

Author:CNR

Publication:HACID web

Date:16.02.2023

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