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Justification This property is used to give a justification for why an ontology has been selected as an exemplary ontology.

This is a property of type Text.

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ATC Ontology +The ontology is a representation of the AT The ontology is a representation of the ATC classification provided by WHO used for the classification of drugs. The concept ATC_Classified_Product represents all the pharmaceutical products classified through the ATC code. This conceptualization of the hierarchy allows inference over the ontology model and obtains the therapeutical, anatomical, pharmacological or chemical group of one determinate pharmaceutical product from its ATC code. pharmaceutical product from its ATC code.
Aggregated Invoice Ontology +AIO (Aggregated Invoice Ontology, http://w AIO (Aggregated Invoice Ontology, is a bundle of all four described ontologies. This bundle models the eInvocing domain with business processes (IBO) for three invoice domains (UBLIO, EIMO and PIIO). This version contains the core and domains of the following ontologies: - IBO (Invoicing Backbone Ontology), This core ontology is based on DUL (DOLCE+DnS Ultralite) and IOLight. Additional concepts, describing business processes are added to modularize the domain of the invoices. - PIIO (PharmaInnova Invoice Ontology, This domain ontoloy is based on the original XML files describing the PharmaInnova Invoice Model. We realized some changes to complete the ontology; adding more properties and restrictions. We combined moreover the concepts of emitted and received invoices to one single concept, because both describe the same real world object of an invoice. - EIMO (Edifact Invoice Mesasge, This domain ontology is based on the international standard language for electronic data interchange for administration, commerce and transport developed under the United Nations. The modularized part is corresponding to the Invoice Message subset of Edifact. - UBLIO (UBL Invoice Ontology, This domain ontology is based on the Universal Business Language, it was developed using the Ontolog UBL ontology . This ontology was part of the old InvoiceOntology. ology was part of the old InvoiceOntology.
Aquatic Resource Observation +This has been built in a pure pattern-base This has been built in a pure pattern-based design, reusing logical, content, and architectural patterns. Due to the nature of the technological problem (data reengineering from XML), and the conceptual problem (statistical abstraction of attributes for dynamical entities), several modelling issues had to be tackled, and have been solved without losing the expressivity of the original data; the solutions actually added the possibility of opening up the data, verifying their integrity and enriching them with implicit or additional inferences. em with implicit or additional inferences.
Association Ontology +The Association Ontology hooks up parts of the Similarity Ontology, the Review Ontology and DCMI Metadata Terms.


CGI Simple Lithology 201001 +Working example of a scientific ontology, demonstrates class definitions to build category hierarchy using Pellet reasoner.
COMM - Core ontology for Multimedia Annotation +Requirements are well spelled out, e.g. MP Requirements are well spelled out, e.g. MPEG-7 compliance, interoperability, separation of concerns, modularity and extensibility. This ontology builds on existing foundational ontologies (DOLCE) and is divided up into modules. It is also very thorough covering a substantial domain with great care. ring a substantial domain with great care.
CaRePa +Without patterns, recurring problem can not be identified for solvation.
Chemical Compound and Chemical Functional Group Ontology +Each compound and chemical functional grou Each compound and chemical functional group can be fully described (e.g. in terms of necessary and sufficient conditions) using lower granular terms (e.g. a compound is defined in terms of its functional groups, functional groups are defined in terms of atoms). nal groups are defined in terms of atoms).
Cognitive Characteristics Ontology +The Cognitive Characteristics Ontology is The Cognitive Characteristics Ontology is built on top of the Weighted Interests Vocabulary v0.5 and should probably substitute this ontology in the near future. That means all concepts and properties are imported from this ontology. Some of them are also redefined and renamed to broaden their meaning. Furthermore, the Cognitive Characteristics Ontology is inspired by the Unified User Context Model, the General User Model Ontology, the User Modelling for Information Retrieval Language and all their fundamental sources, and finally, the discussions on the FOAF developers mailing list. The Weighted Interests Vocabulary v0.5 is an union of the Weighted Interest Vocabulary, the E-foaf:interest Vocabulary and the Interest Mining Ontology. That means, all interest related ontologies are now merged under one hood and some concepts are proper modeled now. The design of this interest ontology is also strongly influenced by the outcome of the User (weighted) Interests Ontology working group from Hypios VoCamp Paris 2010. rking group from Hypios VoCamp Paris 2010.
Counter Ontology +The Counter Ontology is a generalisation of the Playcount Ontology from Yves Raimond.
Countries +Formalizing an ISO standard


DOLCE+DnS Ultralite +DOLCE makes a variety of important distinctions that are useful for upper ontologies. It was developed in a principled way using the OntoClean methodology to help ensure correct and consistent distinctions.


Emotional Knowledge Graph (EmoKG) +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.
Event Model F +Build from existing foundational ontologies. Requirements are carefully spelled out, that derive from one or more scenarios.


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) +Built in a very structured and principled way.


GUM-Space +GUM-Space has been designed on the basis o GUM-Space has been designed on the basis of general findings in language science and empirical research in cognitive linguistics in contexts ranging over formal semantic interpretations, psychological studies, dialogue analysis, and computational modeling. The ontology has thus been designed with respect to results from these fields with respect to the way language structures spatial information. It aims at a thorough and exhaustive description of categories in spatial language. A huge amount of data was analyzed and annotated (instantiated) with the ontology by different users. The evaluation based on inter-annotator agreement can be found at . GUM-Space formalizes only those spatial aspects that are given by the language. Information about lexical or context-dependent aspects are therefore not part of the ontology, as it only meets its intended requirements. As GUM-Space is an extension of GUM, further extensions can be developed and related to GUM or GUM-Space. A full documentation and further information about the ontology is available at: . .
GoodRelations +Many reasons: # Excellent documentation # Excellent use of competency questions throughout # Ontology divided up into conceptual modules # Handle a variety of challenging representation issues # Is already having significant commercial impact
Grid4AllOntology +Re-uses a number of existing conceptualiza Re-uses a number of existing conceptualizations and extends them in such a way (designed for efficient queries' answering) that the retrieval of resources, services, and markets that trade them is achieved through DL reasoning facilitation (classification and computation of inferred types) and some SPARQL quering for post-filtering and ranking of results. for post-filtering and ranking of results.


HCONEadminOnto +Although other Administrative meta-ontologies have been also proposed, they are not part of an ontology framework that supports the collaborative development (and evaluation) of evolving ontologies.
HCONEarguOnto +Although other argumentation meta-ontologies have been also proposed (e.g. DILIGENT argumentation model), they are not part of an ontology framework that supports the collaborative development (and evaluation) of evolving ontologies.
HCONEevolutionOnto +Although other evoluation meta-ontologies have been also proposed, they are not part of an ontology framework that supports the collaborative development (and evaluation) of evolving ontologies


Identity of Resources on the Web +-
Info Service Ontology +There is a need to describe information services of all kind, not only semantic web information services.
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