The Dublin Core, also known as the Dublin Core Metadata Element Set, is a set of fifteen "core" elements (properties) for describing resources. This fifteen-element Dublin Core has been formally standardized as ISO 15836, ANSI/NISO Z39.85, and IETF RFC 5013. The Dublin Core Metadata Initiative (DCMI), which formulates the Dublin Core, is a project of the Association for Information Science and Technology (ASIS&T), a non-profit organization. The core properties are part of a larger set of DCMI Metadata Terms. "Dublin Core" is also used as an adjective for Dublin Core metadata, a style of metadata that draws on multiple Resource Description Framework (RDF) vocabularies, packaged and constrained in Dublin Core application profiles.
The Semantic Web is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.
In computer science and information science, an ontology encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.
The Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax notations and data serialization formats. It is also used in knowledge management applications.
The XML Metadata Interchange (XMI) is an Object Management Group (OMG) standard for exchanging metadata information via Extensible Markup Language (XML).
The Meta-Object Facility (MOF) is an Object Management Group (OMG) standard for model-driven engineering. Its purpose is to provide a type system for entities in the CORBA architecture and a set of interfaces through which those types can be created and manipulated. The official reference page may be found at OMG's website.
The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.
RDF Schema is a set of classes with certain properties using the RDF extensible knowledge representation data model, providing basic elements for the description of ontologies. It uses various forms of RDF vocabularies, intended to structure RDF resources. RDF and RDFS can be saved in a triplestore, then one can entail some knowledge from them using a query language, like SPARQL.
Common Logic (CL) is a framework for a family of logic languages, based on first-order logic, intended to facilitate the exchange and transmission of knowledge in computer-based systems.
The semantic spectrum is a series of increasingly precise or rather semantically expressive definitions for data elements in knowledge representations, especially for machine use.
Simple Knowledge Organization System (SKOS) is a W3C recommendation designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary. SKOS is part of the Semantic Web family of standards built upon RDF and RDFS, and its main objective is to enable easy publication and use of such vocabularies as linked data.
The AgMES initiative was developed by the Food and Agriculture Organization (FAO) of the United Nations and aims to encompass issues of semantic standards in the domain of agriculture with respect to description, resource discovery, interoperability and data exchange for different types of information resources.
Gellish is an ontology language for data storage and communication, designed and developed by Andries van Renssen since mid-1990s. It started out as an engineering modeling language but evolved into a universal and extendable conceptual data modeling language with general applications. Because it includes domain-specific terminology and definitions, it is also a semantic data modelling language and the Gellish modeling methodology is a member of the family of semantic modeling methodologies.
The CIDOC Conceptual Reference Model (CRM) provides an extensible ontology for concepts and information in cultural heritage and museum documentation. It is the international standard (ISO 21127:2014) for the controlled exchange of cultural heritage information. Galleries, libraries, archives, museums (GLAMs), and other cultural institutions are encouraged to use the CIDOC CRM to enhance accessibility to museum-related information and knowledge.
The concept of the Social Semantic Web subsumes developments in which social interactions on the Web lead to the creation of explicit and semantically rich knowledge representations. The Social Semantic Web can be seen as a Web of collective knowledge systems, which are able to provide useful information based on human contributions and which get better as more people participate. The Social Semantic Web combines technologies, strategies and methodologies from the Semantic Web, social software and the Web 2.0.
DOGMA, short for Developing Ontology-Grounded Methods and Applications, is the name of research project in progress at Vrije Universiteit Brussel's STARLab, Semantics Technology and Applications Research Laboratory. It is an internally funded project, concerned with the more general aspects of extracting, storing, representing and browsing information.
The Semantics of Business Vocabulary and Business Rules (SBVR) is an adopted standard of the Object Management Group (OMG) intended to be the basis for formal and detailed natural language declarative description of a complex entity, such as a business. SBVR is intended to formalize complex compliance rules, such as operational rules for an enterprise, security policy, standard compliance, or regulatory compliance rules. Such formal vocabularies and rules can be interpreted and used by computer systems. SBVR is an integral part of the OMG's model-driven architecture (MDA).
In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which are formal representations of a set of concepts within a domain and the relationships between those concepts. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.
Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.
In the Semantic Web and in knowledge representation, a metaclass is a class whose instances are themselves classes. Similar to their role in programming languages, metaclasses in Semantic Web languages can have properties otherwise applicable only to individuals, while retaining the same class's ability to be classified in a concept hierarchy. This enables knowledge about instances of those metaclasses to be inferred by semantic reasoners using statements made in the metaclass. Metaclasses thus enhance the expressivity of knowledge representations in a way that can be intuitive for users. While classes are suitable to represent a population of individuals, metaclasses can, as one of their feature, be used to represent the conceptual dimension of an ontology. Metaclasses are supported in the ontology language OWL and the data-modeling vocabulary RDFS.
