We present the Frame-based ontology Design Outlet (FrODO), a novel method and tool for drafting ontologies from competency questions automatically. Competency questions are expressed as natural language and are a common solution for representing requirements in a number of agile ontology engineering methodologies, such as the eXtreme Design (XD) or SAMOD. FrODO builds on top of FRED. In fact, it leverages the frame semantics for drawing domain-relevant boundaries around the RDF produced by FRED from a competency question, thus drafting domain ontologies. We carried out a user-based study for assessing FrODO in supporting engineers for ontology design tasks. The study shows that FrODO is effective in this and the resulting ontology drafts are qualitative.

Python is an interpreted, object-oriented programming language. Despite it's popularity, it's often accused of being slow. In this course you will learn how to optimize the performance of your Python code. You will learn various tricks to reduce execution time. A lot of people have different definitions of performance.

The Internet of Battlefield Things (IoBT) will advance the operational effectiveness of infantry units. However, this requires autonomous assets such as sensors, drones, combat equipment, and uncrewed vehicles to collaborate, securely share information, and be resilient to adversary attacks in contested multi-domain operations. CAPD addresses this problem by providing a context-aware, policy-driven framework supporting data and knowledge exchange among autonomous entities in a battlespace. We propose an IoBT ontology that facilitates controlled information sharing to enable semantic interoperability between systems. Its key contributions include providing a knowledge graph with a shared semantic schema, integration with background knowledge, efficient mechanisms for enforcing data consistency and drawing inferences, and supporting attribute-based access control. The sensors in the IoBT provide data that create populated knowledge graphs based on the ontology. This paper describes using CAPD to detect and mitigate adversary actions. CAPD enables situational awareness using reasoning over the sensed data and SPARQL queries. For example, adversaries can cause sensor failure or hijacking and disrupt the tactical networks to degrade video surveillance. In such instances, CAPD uses an ontology-based reasoner to see how alternative approaches can still support the mission. Depending on bandwidth availability, the reasoner initiates the creation of a reduced frame rate grayscale video by active transcoding or transmits only still images. This ability to reason over the mission sensed environment and attack context permits the autonomous IoBT system to exhibit resilience in contested conditions.

There is a large number of online documents data sources available nowadays. The lack of structure and the differences between formats are the main difficulties to automatically extract information from them, which also has a negative impact on its use and reuse. In the biomedical domain, the DISNET platform emerged to provide researchers with a resource to obtain information in the scope of human disease networks by means of large-scale heterogeneous sources. Specifically in this domain, it is critical to offer not only the information extracted from different sources, but also the evidence that supports it. This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations; with the objective of providing an schema to improve the publication and description of evidences and biomedical associations in this domain. The ontology has been successfully evaluated to ensure there are no errors, modelling pitfalls and that it meets the previously defined functional requirements. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed according to the proposed ontology to create a Knowledge Graph that can be used in real scenarios, and which has also been used for the evaluation of the presented ontology.

The importance of taking individual, potentially conflicting perspectives into account when dealing with knowledge has been widely recognised. Many existing ontology management approaches fully merge knowledge perspectives, which may require weakening in order to maintain consistency; others represent the distinct views in an entirely detached way. As an alternative, we propose Standpoint Logic, a simple, yet versatile multi-modal logic "add-on" for existing KR languages intended for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints, which can be hierarchically organised, combined and put in relation to each other. Starting from the generic framework of First-Order Standpoint Logic (FOSL), we subsequently focus our attention on the fragment of sentential formulas, for which we provide a polytime translation into the standpoint-free version. This result yields decidability and favourable complexities for a variety of highly expressive decidable fragments of first-order logic. Using some elaborate encoding tricks, we then establish a similar translation for the very expressive description logic SROIQb_s underlying the OWL 2 DL ontology language. By virtue of this result, existing highly optimised OWL reasoners can be used to provide practical reasoning support for ontology languages extended by standpoint modelling.

Information on cyber-related crimes, incidents, and conflicts is abundantly available in numerous open online sources. However, processing the large volumes and streams of data is a challenging task for the analysts and experts, and entails the need for newer methods and techniques. In this article we present and implement a novel knowledge graph and knowledge mining framework for extracting the relevant information from free-form text about incidents in the cyberdomain. The framework includes a machine learning based pipeline for generating graphs of organizations, countries, industries, products and attackers with a non-technical cyber-ontology. The extracted knowledge graph is utilized to estimate the incidence of cyberattacks on a given graph configuration. We use publicly available collections of real cyber-incident reports to test the efficacy of our methods. The knowledge extraction is found to be sufficiently accurate, and the graph-based threat estimation demonstrates a level of correlation with the actual records of attacks. In practical use, an analyst utilizing the presented framework can infer additional information from the current cyber-landscape in terms of risk to various entities and propagation of the risk heuristic between industries and countries.

The quality of ontologies in terms of their correctness and completeness is crucial for developing high-quality ontology-based applications. Traditional debugging techniques repair ontologies by removing unwanted axioms, but may thereby remove consequences that are correct in the domain of the ontology. In this paper we propose an interactive approach to mitigate this for $\mathcal{EL}$ ontologies by axiom weakening and completing. We present algorithms for weakening and completing and present the first approach for repairing that takes into account removing, weakening and completing. We show different combination strategies, discuss the influence on the final ontologies and show experimental results. We show that previous work has only considered special cases and that there is a trade-off between the amount of validation work for a domain expert and the quality of the ontology in terms of correctness and completeness.

Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data. We alleviate this need by aligning individually pre-trained language and vision representation models using a much smaller amount of paired data, augmented with a curriculum learning algorithm to learn fine-grained vision-language alignments. TOnICS (Training with Ontology-Informed Contrastive Sampling) initially samples minibatches whose image-text pairs contain a wide variety of objects to learn object-level alignment, and progressively samples minibatches where all image-text pairs contain the same object to learn finer-grained contextual alignment. Aligning pre-trained BERT and VinVL models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval while using less than 1% as much training data.

Despite the large-scale uptake of semantic technologies in the biomedical domain, little is known about common modelling practices in published ontologies. OWL ontologies are often published only in the crude form of sets of axioms leaving the underlying design opaque. However, a principled and systematic ontology development life cycle is likely to be reflected in regularities of the ontology's emergent syntactic structure. To develop an understanding of this emergent structure, we propose to reverse-engineer ontologies taking a syntax-directed approach for identifying and analysing regularities for axioms and sets of axioms. We survey BioPortal in terms of syntactic modelling trends and common practices for OWL axioms and class frames. Our findings suggest that biomedical ontologies only share simple syntactic structures in which OWL constructors are not deeply nested or combined in a complex manner. While such simple structures often account for large proportions of axioms in a given ontology, many ontologies also contain non-trivial amounts of more complex syntactic structures that are not common across ontologies.

Controlled Query Evaluation (CQE) has been recently studied in the context of Semantic Web ontologies. The goal of CQE is concealing some query answers so as to prevent external users from inferring confidential information. In general, there exist multiple, mutually incomparable ways of concealing answers, and previous CQE approaches choose in advance which answers are visible and which are not. In this paper, instead, we study a dynamic CQE method, namely, we propose to alter the answer to the current query based on the evaluation of previous ones. We aim at a system that, besides being able to protect confidential data, is maximally cooperative, which intuitively means that it answers affirmatively to as many queries as possible; it achieves this goal by delaying answer modifications as much as possible. We also show that the behavior we get cannot be intensionally simulated through a static approach, independent of query history. Interestingly, for OWL 2 QL ontologies and policy expressed through denials, query evaluation under our semantics is first-order rewritable, and thus in AC0 in data complexity. This paves the way for the development of practical algorithms, which we also preliminarily discuss in the paper.