GoodRelations is a standardized vocabulary (also known as “schema”, “data dictionary”, or “ontology”) for product, price, store, and company data that can (1) be embedded into existing static and dynamic Web pages and that (2) can be processed by other computers. This increases the visibility of your products and services in the latest generation of search engines, recommender systems, and other novel applications.
The dataset contains linked data that is transformed from RailML. The data that is transformed is a dataset containing railML datapoints from the northwest part of Germany. Here two train networks overlap. The RINF train network and EVB trainnetwork. The image below showcases the train network that is contained in the railML assets.
The data is extracted according to the ERA vocabulary.
This dataset contains the vocabulary of the ERA knowledge graph. The vocabulary of the ERA knowledge graph consists out of two different sources.
- The ontology.Vocabulary defined by the European Union Agency for Railways to describe the concepts and relationships related to the European railway infrastructure and the vehicles authorized to operate over it. The file is synchronized on 2022-02-02.
- The skos Concepts. Controlled SKOS-based vocabularies defined by the European Union Agency for Railways to describe concepts related to the European railway infrastructure and the vehicles authorized to operate over it. The file is synchronized on 2022-02-02.
This vocabulary allows versions to be identified for arbitrary resources.
Explanation of this vocabulary
This vocabulary introduces the following two version types:
Version instances are related to the resources with the following property:
Sometimes, we need to describe scenarios in which someone (e.g., a person) has a value (e.g., a particular role) during a particular time and for a particular context. Four different things are involved in these kinds of scenarios:
- the entity having some value, e.g. a person or a document possessing a role or a status;
- the value had by someone, e.g. a role or a status;
- the time period during which the entity has that value, e.g. from April 2008 to September 2008;
- the particular context that characterises the act of having that value, e.g. being a member of an institution or an editor of a particular journal.
Thought as natural extension of the Time-indexed Situation pattern, this ontological pattern, called Time-indexed Value in Context (TVC), is able to describe these kinds of scenarios.
Recently within the Semantic Web community a new topic has been actively discussed: whether and how to allow literals to be subjects of RDF statements (link). This discussions failed to provide a unique and clear indication of how to proceed in that regard.
Although one of the suggestions coming out of the discussion was to explicitly include the proposal in a (future) specification of RDF, this topic is not actually new, particularly in ontology modelling. The needs to describe “typical” literals (specially strings) as individuals of a particular class has been addressed by a lot of models in past, such as Common Tag (through the class Tag), SIOC (through the classes Category and Tag), SKOS-XL (through the class Label), and LMM (through the class Expression).
After considering the above-mentioned models and other related and inspiring ones, this literal reification vocabulary was created to address this issue. It allows to express literal values as proper ontological individuals so as to use them as subject/object of any assertion within OWL models.
The Error Ontology is an unit test that allow to produce an inconsistent model if a particular (and incorrect) situation happens.
It works by means of a data property, ‘hasError’ that denies its usage for ant resource. In fact, by defining its domain as “all those resource that don't have any hasErrorDescription assertion”, a resource that asserts having an error makes the ontology inconsistent.
Stabiler Link auf den aktuellen Gesamtabzug ZDB-Titeldaten.
The German Union Catalogue of Serials (ZDB) is one of the world's largest databases dedicated to journals, newspapers, serials and other periodically issued publications from all countries, in all languages, with no time restrictions and in printed, electronic or other forms.
The ZDB currently contains over 1.8 million bibliographic records with more than 16.5 million holding records dating from 1500 to the present day. These were compiled in cooperation with around 3,700 German and Austrian libraries.
The Staatsbibliothek zu Berlin – Preußischer Kulturbesitz and the German National Library are responsible for the maintenance and the further development of the ZDB as equal partners.
An RDF representation of XHTML 2.0's built-in link types. It is based on the Metainformation Attributes Module of XHTML2, and uses W3C's RDF Schema (RDFS) and Web Ontology (OWL) languages to describe several kinds of relationship that can hold between information resources.
RDF documents can make any number of statements. Without some kind of signature or other similar verification mechanism, there is no way to understand who made these statements. One way to document who made a set of statements is via the use of Digital Signatures: signing a document using Public Key Cryptography. The WOT, or Web Of Trust, schema is designed to facilitate the use of Public Key Cryptography tools such as PGP or GPG to sign RDF documents and document these signatures.
Protocol for Web Description Resources (POWDER): POWDER-S Vocabulary (WDRS)
The Protocol for Web Description Resources (POWDER) allows metadata to be associated with groups of resources such as those found on a Web site. Its main 'unit of information' is the Description Resource (DR), one or more of which are contained in a POWDER document. Processing such a document yields RDF triples describing the resources that are within the scope of the DRs. POWDER documents are written in XML and have relatively loose semantics, however, a GRDDL transform, associated with the root namespace, renders the data in OWL with more formal semantics.
An RDF vocabulary is defined to support Semantic POWDER, or POWDER-S encoding, of Description Resources. This is the namespace document for that vocabulary. Although this document sets out the domain and range of each term, their full definitions are to be found in other documents in the set, notably the Description Resources document DR and the Formal Semantics document FORMAL.
VoID is an RDF Schema vocabulary for expressing metadata about RDF datasets. It is intended as a bridge between the publishers and users of RDF data, with applications ranging from data discovery to cataloging and archiving of datasets. This document is a detailed guide to the VoID vocabulary. It describes how VoID can be used to express general metadata based on Dublin Core, access metadata, structural metadata, and links between datasets. It also provides deployment advice and discusses the discovery of VoID descriptions.
VOAF is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies) used in the Linked Data Cloud. In particular it provides properties expressing the different ways such vocabularies can rely on, extend, specify, annotate or otherwise link to each other. It relies itself on Dublin Core and VoID. The name of the vocabulary makes an explicit reference to FOAF because VOAF can be used to define networks of vocabularies in a way similar to the one FOAF is used to define networks of people.
A mapping of the vCard specification to RDF/OWL. The goal is to promote the use of vCard for the description of people and organizations utilizing semantic web techniques and allowing compatibility with traditional vCard implementations.
This is the Variation and Translation module of the Lemon collection of lexicographic vocabularies. It can be used to represent relations between lexical entries and lexical senses that are variants of each other.
A vocabulary for annotating descriptions of vocabularies with examples and usage notes.
The purpose of VAEM is to provide, by import, a foundation for commonly needed resources for metadata on an ontology.
VAEM stands for “Vocabulary for Attaching Essential Metadata”. What VAEM regards as essential metadata is data about dates and times, confidentiality, and other characterisitic qualifiers of the ontology, but also references to where a ontology is documented and where to find ontology registration for governance, attribution and provenance. VAEM makes use of some properties from the Dublin Core Terms vocabulary.
- An upper ontology of about 35,000 reference concepts, designed to provide a common grounding for relating different ontologies or schema to one another, and
- A vocabulary for aiding ontology and data mapping, including expressions of likelihood relationships distinct from exact identity or equivalence. This vocabulary is also designed for interoperating amongst various domain ontologies.
AAT is a structured, multilingual vocabulary including terms, descriptions, and other information for generic concepts related to art, architecture, other cultural heritage, and conservation. For decades now, the AAT has been used as a primary reference by museums, art libraries, archives, visual resource catalogers, conservation specialists, archaeological projects, bibliographic projects, researchers, and information specialists who are dealing with the needs of these users.
Terms for any concept may include the plural form of the term, singular form, natural order, inverted order, spelling variants, scientific and common forms, various forms of speech, and synonyms that have various etymological roots. Among these terms, one is flagged as the term (or descriptor) preferred by the Getty Vocabulary Program. There may be multiple descriptors reflecting usage in multiple languages. Preferences for individual contributors may differ and are noted.
The AAT is a thesaurus in compliance with ISO and NISO standards.
The focus of each AAT record is a concept. Linked to each concept are terms, related concepts, its position in the hierarchy, sources for the data, and notes. The conceptual framework of facets and hierarchies in the AAT is designed to allow a general classification scheme for art, architecture and conservation. The framework is not subject-specific; for example, there is no defined portion of the AAT that is specific only for Renaissance painting. The terms to describe Renaissance paintings will be found in many locations in the AAT hierarchies. There may be multiple broader contexts, making AAT polyhierarchical. In addition the AAT has equivalence and associative relationships. The temporal coverage of the AAT ranges from Antiquity to the present and the scope is global.
The User Interface ontology allows the definition of forms for processing RDF model data, and include a bootstrap form for editing forms. It allows user interface hints such as background colors, can be associated with objects and classes.
This is a draft RDF vocabulary for representing spatial data sets in INSPIRE as RDF. This vocabulary has been created using the “Guidelines for the RDF encoding of spatial data”.
The use of RDF is optional and does not supersede or replace the requirements regarding encoding specified in Clause 9 of the INSPIRE Data Specifications. This optional encoding is intended to support the e-government and open data community in Europe, which is increasingly looking at RDF to represent data.
This is a draft version. It has limitations and is expected to contain errors. Please report any issues or concerns in GitHub.
This ontology contains classes and properties that have been derived from the INSPIRE Common Transport Elements application schema.
During the derivation, the following mappings, alignments, and omissions have been applied:
- Code list and enumeration values are mapped to
- The properties
validToare mapped to the global properties defined by the base ontology.
- Geometry types are mapped to classes from the Simple Feature ontology.
- Code list and enumeration values are mapped to
Alignments (through subsumption):
- Spatial object types are aligned with
- Properties of spatial object types with value type
GeographicalNameare aligned to property
- Properties with a geometry value type are aligned to
- Spatial object types are aligned with
inspireIdis omitted. See the guidelines for further details.
OWL-Time is an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. The ontology provides a vocabulary for expressing facts about topological (ordering) relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional (Gregorian) calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars.
The time interval pattern. This pattern is extracted from DOLCE-UltraLite by partial clone of elements and expansion. Two datatype properties have been added which allow to express the boundaries of the time interval. Ranges of datatype properties are not indicated, because otherwise specializing them with different xsd datatypes would lead to inconsistencies.
This guide is intended to facilitate the use of Darwin Core terms in the Resource Description Framework (RDF). It explains basic features of RDF and provides details of how to expose data in the form of RDF using Darwin Core terms and terms from other key vocabularies. It defines terms in the namespace http://rs.tdwg.org/dwc/iri/ which are intended for use excusively with non-literal objects.