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OntologyPurpose Brief description of what the ontology is for and what the intended benefits are to whom. This is a property of type Text.

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Pages using the property "OntologyPurpose"

Showing 25 pages using this property.


Aggregated Invoice Ontology +This ontology is a bundle of four ontologies. This bundle models the eInvocing domain with business processes for PharmaInnova case study in the NeOn project.
Aquatic Resource Observation +To represent the aquatic observation data To represent the aquatic observation data extracted from factsheet application. An aquatic observation refers to a specific year, is about an aquatic resource, and is situated in a given habitat, in which the aquatic species from the resource live. Observations can have a reporting year that is different from the observation time. at is different from the observation time.
Association Ontology +Providing various general association relation types and a concept for feedback-able personal associations.


CGI Simple Lithology 201001 +Categorization of rocks, logical check of CGI SimpleLithology 201001 SKOS vocabulary
COMM - Core ontology for Multimedia Annotation +To increase the interoperability of applications producing and consuming multimedia annotations.
CaRePa +To Serve the requirement elicitation of the context aware system
Chemical Compound and Chemical Functional Group Ontology +Chemical classification is critically impo Chemical classification is critically important in chemistry and in drug discovery. The purpose of the ontology is to classify compounds. It demonstrates how we can use an OWL reasoner over a chemical knowledge base to classify compounds based on the description of their chemical structure. For instance, the ontology can be used to classify ethanol as an alcohol because ethanol contains a set of carbon, oxygen and hydrogen atoms that are connected in such a way that on reasoning, it is discovered to contain a hydroxyl group (R-O-H) which is a necessary and sufficient condition to be a kind of alcohol. ficient condition to be a kind of alcohol.
Cognitive Characteristics Ontology +Description of personal cognitive pattern in a simple and extended way.
Counter Ontology +To describe counters of all kinds and events that are related to a specific counter.
Countries +Formalizing an ISO standard for general use


DOLCE+DnS Ultralite +To provide a set of upper level concepts that can be the basis for easier interoperability among many middle and lower level ontologies.


EchoCareer Ontology +It is developed a Job Recommender Ontology for Hearing Impaired Individuals (EchoCareer), which can integrate with JRS solutions.
Emotional Knowledge Graph (EmoKG) +Humans are feeling emotions every day, but Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health. Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health. Amelie Gyrard and Karima Boudaoud. MDPI Applied Sciences 2022. Special Issue Affective Computing and Recommender Systems. fective Computing and Recommender Systems.
Event Model F +Interoperability in distributed event-based systems


FIESTA-IoT +FIESTA-IoT ontology takes inspiration from FIESTA-IoT ontology takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To bu0ild the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbed adapt their respective datasets to the developed ontology. R. Agarwal, D. Fernandez, T. Elsaleh, A. Gyrard, J. Lanza, L. Sanchez, N. Georgantas, V. Issarny, "Unified IoT Ontology to Enable Interoperability and Federation of Testbeds", 3rd IEEE World Forum on IoT, pp. 70-75, Reston, USA, 12-14 December 2016. DOI: 10.1109/WF-IoT.2016.7845470, IEEE, HAL OI: 10.1109/WF-IoT.2016.7845470, IEEE, HAL
Foundational Model of Anatomy (FMA) +To make anatomical information in symbolic form available to knowledge modelers and other developers of applications for education, clinical medicine, electronic health record, biomedical research and all areas of health care delivery and management.


GUM-Space +GUM-Space supports natural language proces GUM-Space supports natural language processing for spatial language while simplifying the interface between domain-specific knowledge and general linguistic resources. The general nature and features of the ontology's specification of the semantics of spatial language expressions offers a substantial simplification of the general problem of relating natural spatial language to its conceptualized interpretation. uage to its conceptualized interpretation.
GoodRelations +To facilitate creation of formal descriptions of product offerings for electronic commerce.
Grid4AllOntology +To serve as a semantic registry of Semantic Information System (SIS) in Grid4All project.


HCONEadminOnto +To allow the representation of administrat To allow the representation of administrative information in the context of HCOME-3O meta-ontology framework that implements HCOME ontology engineering methodology for the collaborative developing of evolving ontologies. It is interlinked with other two meta-ontologies for Argumentation and Evolution meta-information representation. Evolution meta-information representation.
HCONEarguOnto +To allow the representation of argumentati To allow the representation of argumentation information in the context of HCOME-3O meta-ontology framework that implements HCOME ontology engineering methodology for the collaborative developing of evolving ontologies. It is interlinked with other two meta-ontologies for Administration and Evolution meta-information representation. The argumentation model is an extended version of the IBIS model. is an extended version of the IBIS model.
HCONEevolutionOnto +To allow the representation of changes bet To allow the representation of changes between ontology versions in the context of HCOME-3O meta-ontology framework that implements HCOME ontology engineering methodology for the collaborative developing of evolving ontologies. It is interlinked with other two meta-ontologies for Argumentation and Administration meta-information representation. istration meta-information representation.


Identity of Resources on the Web +-
Info Service Ontology +For describing, rating and categorizing information services of all kind.
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