Extracting relevant and structured knowledge from large, complex technical documents within the Reliability and Maintainability (RAM) domain is labor-intensive and prone to errors. Our work addresses this challenge by presenting OntoKGen, a genuine pipeline for ontology extraction and Knowledge Graph (KG) generation. OntoKGen leverages Large Language Models (LLMs) through an interactive user interface guided by our adaptive iterative Chain of Thought (CoT) algorithm to ensure that the ontology extraction process and, thus, KG generation align with user-specific requirements. Although KG generation follows a clear, structured path based on the confirmed ontology, there is no universally correct ontology as it is inherently based on the user's preferences. OntoKGen recommends an ontology grounded in best practices, minimizing user effort and providing valuable insights that may have been overlooked, all while giving the user complete control over the final ontology. Having generated the KG based on the confirmed ontology, OntoKGen enables seamless integration into schemeless, non-relational databases like Neo4j. This integration allows for flexible storage and retrieval of knowledge from diverse, unstructured sources, facilitating advanced querying, analysis, and decision-making. Moreover, the generated KG serves as a robust foundation for future integration into Retrieval Augmented Generation (RAG) systems, offering enhanced capabilities for developing domain-specific intelligent applications.

Ontology-based knowledge graphs (KG) are desirable for effective knowledge management and reuse in various decision making scenarios, including design. Creating and populating extensive KG based on specific ontological models can be highly labour and time-intensive unless automated processes are developed for knowledge extraction and graph creation. Most research and development on automated extraction and creation of KG is based on extensive unstructured data sets that provide contextual information. However, some of the most useful information about the products and services of a company has traditionally been recorded as structured data. Such structured data sets rarely follow a standard ontology, do not capture explicit mapping of relationships between the entities, and provide no contextual information. Therefore, this research reports a method and digital workflow developed to address this gap. The developed method and workflow employ rule-based techniques to extract and create a Function Behaviour-Structure (FBS) ontology-based KG from legacy structured data, especially specification sheets and product catalogues. The solution approach consists of two main components: a process for deriving context and context-based classification rules for FBS ontology concepts and a workflow for populating and retrieving the FBS ontology-based KG. KG and Natural Language Processing (NLP) are used to automate knowledge extraction, representation, and retrieval. The workflow's effectiveness is demonstrated via pilot implementation in an industrial context. Insights gained from the pilot study are reported regarding the challenges and opportunities, including discussing the FBS ontology and concepts.

In this paper we present a hybrid method for the automatic detection of dermatological pathologies in medical reports. We use a large language model combined with medical ontologies to predict, given a first appointment or follow-up medical report, the pathology a person may suffer from. The results show that teaching the model to learn the type, severity and location on the body of a dermatological pathology, as well as in which order it has to learn these three features, significantly increases its accuracy. The article presents the demonstration of state-of-the-art results for classification of medical texts with a precision of 0.84, micro and macro F1-score of 0.82 and 0.75, and makes both the method and the data set used available to the community.

Ontology learning in complex domains, such as life sciences, poses significant challenges for current Large Language Models (LLMs). Existing LLMs struggle to generate ontologies with multiple hierarchical levels, rich interconnections, and comprehensive class coverage due to constraints on the number of tokens they can generate and inadequate domain adaptation. To address these issues, we extend the NeOn-GPT pipeline for ontology learning using LLMs with advanced prompt engineering techniques and ontology reuse to enhance the generated ontologies' domain-specific reasoning and structural depth. Our work evaluates the capabilities of LLMs in ontology learning in the context of highly specialized and complex domains such as life science domains. To assess the logical consistency, completeness, and scalability of the generated ontologies, we use the AquaDiva ontology developed and used in the collaborative research center AquaDiva as a case study. Our evaluation shows the viability of LLMs for ontology learning in specialized domains, providing solutions to longstanding limitations in model performance and scalability.

Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers both what a model does; e.g., can it be used to reveal personal information from its training set, and how a model was built; e.g., was it only trained on licensed data sets. Determining the safety of an AI system requires gathering information from a wide set of heterogeneous sources including safety benchmarks and technical documentation for the set of models used in that system. In addition, responsible use is encouraged through mechanisms that advise and help the user to take mitigating actions where safety risks are detected. We present Usage Governance Advisor which creates semi-structured governance information, identifies and prioritizes risks according to the intended use case, recommends appropriate benchmarks and risk assessments and importantly proposes mitigation strategies and actions.

Ontologies often lack explicit disjointness declarations between classes, despite their usefulness for sophisticated reasoning and consistency checking in Knowledge Graphs. In this study, we explore the potential of Large Language Models (LLMs) to enrich ontologies by identifying and asserting class disjointness axioms. Our approach aims at leveraging the implicit knowledge embedded in LLMs, using prompt engineering to elicit this knowledge for classifying ontological disjointness. We validate our methodology on the DBpedia ontology, focusing on open-source LLMs. Our findings suggest that LLMs, when guided by effective prompt strategies, can reliably identify disjoint class relationships, thus streamlining the process of ontology completion without extensive manual input. For comprehensive disjointness enrichment, we propose a process that takes logical relationships between disjointness and subclass statements into account in order to maintain satisfiability and reduce the number of calls to the LLM. This work provides a foundation for future applications of LLMs in automated ontology enhancement and offers insights into optimizing LLM performance through strategic prompt design. Our code is publicly available on GitHub at https://github.com/n28div/llm-disjointness.

We present High-Throughput Hypothesis Evaluation in Description Logic (HT-HEDL). HT-HEDL is a high-performance hypothesis evaluation engine that accelerates hypothesis evaluation computations for inductive logic programming (ILP) learners using description logic (DL) for their knowledge representation; in particular, HT-HEDL targets accelerating computations for the $\mathcal{ALCQI}^{\mathcal{(D)}}$ DL language. HT-HEDL aggregates the computing power of multi-core CPUs with multi-GPUs to improve hypothesis computations at two levels: 1) the evaluation of a single hypothesis and 2) the evaluation of multiple hypotheses (i.e., batch of hypotheses). In the first level, HT-HEDL uses a single GPU or a vectorized multi-threaded CPU to evaluate a single hypothesis. In vectorized multi-threaded CPU evaluation, classical (scalar) CPU multi-threading is combined with CPU's extended vector instructions set to extract more CPU-based performance. The experimental results revealed that HT-HEDL increased performance using CPU-based evaluation (on a single hypothesis): from 20.4 folds using classical multi-threading to $\sim85$ folds using vectorized multi-threading. In the GPU-based evaluation, HT-HEDL achieved speedups of up to $\sim38$ folds for single hypothesis evaluation using a single GPU. To accelerate the evaluation of multiple hypotheses, HT-HEDL combines, in parallel, GPUs with multi-core CPUs to increase evaluation throughput (number of evaluated hypotheses per second). The experimental results revealed that HT-HEDL increased evaluation throughput by up to 29.3 folds using two GPUs and up to $\sim44$ folds using two GPUs combined with a CPU's vectorized multi-threaded evaluation.

We present SPILDL, a Scalable and Parallel Inductive Learner in Description Logic (DL). SPILDL is based on the DL-Learner (the state of the art in DL-based ILP learning). As a DL-based ILP learner, SPILDL targets the $\mathcal{ALCQI}^{\mathcal{(D)}}$ DL language, and can learn DL hypotheses expressed as disjunctions of conjunctions (using the $\sqcup$ operator). Moreover, SPILDL's hypothesis language also incorporates the use of string concrete roles (also known as string data properties in the Web Ontology Language, OWL); As a result, this incorporation of powerful DL constructs, enables SPILDL to learn powerful DL-based hypotheses for describing many real-world complex concepts. SPILDL employs a hybrid parallel approach which combines both shared-memory and distributed-memory approaches, to accelerates ILP learning (for both hypothesis search and evaluation). According to experimental results, SPILDL's parallel search improved performance by up to $\sim$27.3 folds (best case). For hypothesis evaluation, SPILDL improved evaluation performance through HT-HEDL (our multi-core CPU + multi-GPU hypothesis evaluation engine), by up to 38 folds (best case). By combining both parallel search and evaluation, SPILDL improved performance by up to $\sim$560 folds (best case). In terms of worst case scenario, SPILDL's parallel search doesn't provide consistent speedups on all datasets, and is highly dependent on the search space nature of the ILP dataset. For some datasets, increasing the number of parallel search threads result in reduced performance, similar or worse than baseline. Some ILP datasets benefit from parallel search, while others don't (or the performance gains are negligible). In terms of parallel evaluation, on small datasets, parallel evaluation provide similar or worse performance than baseline.

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in wireless communication, yet accurate channel loss prediction remains a significant challenge, limiting resource optimization performance. To address this issue, this paper leverages Artificial Intelligence Generated Content (AIGC) for the efficient construction of Channel Knowledge Maps (CKM) and UAV trajectory design. Given the time-consuming nature of channel data collection, AI techniques are employed in a Wasserstein Generative Adversarial Network (WGAN) to extract environmental features and augment the data. Experiment results demonstrate the effectiveness of the proposed framework in improving CKM construction accuracy. Moreover, integrating CKM into UAV trajectory planning reduces channel gain uncertainty, demonstrating its potential to enhance wireless communication efficiency.

This paper presents a new ontology that implements the well-known Deontic Traditional Scheme in RDFs and SPARQL, fit to handle irresolvable conflicts, i.e., situations in which two or more statements prescribe conflicting obligations, prohibitions, or permissions, with none of them being "stronger" than the other one(s). In our view, this paper marks a significant advancement in standard theoretical research in formal Deontic Logic. Most contemporary approaches in this field are confined to the propositional level, mainly focus on the notion of obligation, and lack implementations. The proposed framework is encoded in RDF, which is not only a first-order language but also the most widely used knowledge representation language, as it forms the foundation of the Semantic Web. Moreover, the proposed computational ontology formalizes all deontic modalities defined in the Deontic Traditional Scheme, without specifically focusing on obligations, and offers constructs to model and reason with various types of irresolvable conflicts, violations, and the interaction between deontic modalities and contextual constraints in a given state of affairs. To the best of our knowledge, no existing approach in the literature addresses all these aspects within a unified integrated framework. All examples presented and discussed in this paper, together with Java code and clear instructions to re-execute them locally, are available at https://github.com/liviorobaldo/conflict-tolerantDeonticTraditionalScheme