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The GLACIATION platform develops a novel Distributed Knowledge Graph (DKG) that stretches across the edge-core-cloud architecture to reduce energy consumption, improving data processing and optimizing data movement operations. Towards this aim, the platform needs to consume the data and metadata that are fed into the DKG. The metadata can affect and inform the decision-making processes in the GLACIATION architecture and introduces the GLACIATION Metadata Reference Model that will be used for modelling the metadata in the DKG. The GLACIATION Reference Metadata Model makes data ingestion and processing interoperable inside the GLACIATION platform. Linked Data allows for a high level of flexibility and to tackle the variety and merging issues that emerge in heterogenous environments, especially due to the wide range of sensors and other data sources that the platform may integrate. The GLACIATION Reference Metadata Model is tailored to fit the specific purposes of the GLACIATION platform, while the GLACIATION use cases define the scope of the model for better interoperability. There are common metadata challenges for all use cases. This stems from the use of the Kubernetes orchestration system as a basis for the GLACIATION platform. In addition, common to the platform is the ingestion of data from other sources into the DKG that can then be used to affect processing decisions. There are direct data flows from sensors within the system, but also data and metadata from sources external to the system. This allows the system to react e.g. to environmental situations like weather or temperature, but also to requirements concerning security or privacy. Exemplary uses and specializations of the reference model to the GLACIATION use cases are also provided. The GLACIATION Metadata Reference Model can be used for scheduling and performing tasks. The model can be considered as a general conceptualization of a tasks scheduling problem that considers various measuring indicators over the deployed resources. It captures the assignment of time-constrained tasks to time constrained and energy consuming resources, that can satisfy various hard and soft constraints, even compositions of such constraints. The tasks can be monitored through various measuring resources using a variety of single or aggregated, predicted or real measurements. The model is generic, by being both domain and application independent, describing the scheduling tasks, without providing specific solutions on how they can be solved. It can be easily adjusted to each of the current three GLACIATION use cases, covering also the Kubernetes orchestration and its Telemetry System deployed by the project. The proposed model makes it feasible to answer the competency queries defined by each of the Glaciation's use case. @en
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This vocabulary defines a number of concepts peculiar to content strategy which are not accounted for by other vocabularies. @en
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This ontology models personalized tourist experiences by representing cities, points of interest, events, accommodations, restaurants, transportation, and their relationships. This ontology is part of a university project. @en
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The ontology describes the main concepts in the field of education and the connections between them. The current version emphasizes the details of the study material, learning outcomes and the curriculum. @en
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The Crime Event Model is an ontology for the representation of crime events extracted from local newspapers. It could be employed for Crime Analysis purposes: extracting crime information from newspapers and enriching them with proper machine-readable semantics is a critical task to help law enforcement agencies at preventing crime, supporting criminal investigations and evaluating the action of law enforcement agencies themselves. The model is based on the fundamental 5W1H journalistic questions, that are Who?, What?, When?, Where?, Why? and How?. Another important requirement was the attempt to exploit existing knowledge graphs and ontologies such as the Simple Event Model (SEM) Ontology and the Schema.org data model for interoperability and interconnection. @en
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The notion of territory plays a major role in human and social sciences. In an historical context, most approaches are irrelevant as they rely on geometric data, which is not available. In order to represent historical territories,we conceived the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. This approach relies on a notion of building blocks to replace polygonal geometry @en
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The General Data Protection Regulation (GDPR) is comprised of several articles, each with points that refer to specific concepts. The general convention of referring to these points and concepts is to quote the specific article or point using a human-readable reference. This ontology provides a way to refer to the points within the GDPR using the EurLex ontology published by the European Publication Office. It also defines the concepts defined, mentioned, and requried by the GDPR using the Simple Knowledge Organization System (SKOS) ontology. @en
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Metadata vocabularies are used in various domains of study. It provides an in-depth description of the resources. In this work, we develop Algorithm Metadata Vocabulary (AMV), a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). The snag faced by the researchers in the current time is the failure of getting relevant results when searching for algorithms in any search engine. AMV is represented as a semantic model and produced OWL file, which can be directly used by anyone interested to create and publish algorithm metadata as a knowledge graph, or to provide metadata service through SPARQL endpoint. To design the vocabulary, we propose a well-defined methodology, which considers real issues faced by the algorithm users and the practitioners. The evaluation shows a promising result. @en
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The Cultural Event module models cultural events, i.e. events involving cultural properties. @en
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This document is a vocabulary to describe compound measures, i.e. measures with several metric or item that are organized with serveral dimensions. The description of such a measure relies on a Tree-Structure of Requirement (TSoR): a set of requirements structured hierarchicaly with analysis element. A TSoR represents the main measure. Several information may be added to explicitely indicate how the overall score on the measure should be calculated based on the hierarchy, relative importance of the node of the hierarchy and an aggregation function. The measure can be described completely and unambiguously from the organisation to the requirements and the implementation. @en
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An ontology that represents concepts in the legal domain. @en
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An ontology for describing changes between OWL ontology versions @en
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The BDI Ontology provides a formal framework to model the Belief-Desire-Intention (BDI) architecture for rational agents. It defines key mental states—Beliefs, Desires, and Intentions—and their relationships, capturing the agent’s reasoning, motivation, and commitment to action. Supporting classes include Propositions (content of mental states), Justifications (rationale for mental states), Plans (action sequences for goals), and TimeIntervals (temporal validity of entities). Key properties like hasBelief, hasDesire, and hasIntention link agents to mental states, while fulfills, adoptsIntention, and motivatesDesire model dynamic interactions. Temporal properties enable reasoning about time-sensitive states and plans. Axioms ensure consistency, such as disjointness between mental states and domain-specific constraints. This ontology supports reasoning, querying, and analysis of agent behaviour, enabling applications in AI, multi-agent systems, and decision support. @en
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CHAMEO is a domain ontology designed to model the common aspects across the different characterisation techniques and methodologies. @en
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The Geometry Metadata Ontology contains terminology to Coordinate Systems (CS), length units and other metadata (file size, software of origin, etc.). GOM is designed to be at least compatible with OMG (Ontology for Managing Geometry) and FOG (File Ontology for Geometry formats), and their related graph patterns. In addition, GOM provides terminology for some experimental data structures to manage (marked as vs:term_status = unstable): * transformed geometry (e.g. a prototype door geometry that is reused for all doors of this type). This is closely related to the transformation of Coordinate Systems @en