Terminology

A record that registers the preferred form of a personal or corporate name, geographic region or subject term. It may indicate variant forms of the established heading; biographical or cultural information associated with the heading, as well as related headings.

Sets, collections, classes, types of objects, kinds of things.

An academic field dealing with the statistical and/or rule-based modeling of natural language from a computational perspective.

A schema that defines data (including metadata) structures, including the types of elements, sub-elements and values they contain.

Sets of predefined, authorized and curated terms that have been preselected by the designer of the vocabulary. In the organization of information, a pick list or drop-down list of authorized terms is a good example of a controlled vocabulary. Often lists of department names are presented as a controlled vocabulary so there is consistency in the use of terms.

Information describing the intellectual content of the object, such as cataloguing records, finding aids or similar schemes

Metadata that is maintained and sorted within the object it describes, the opposite of stand-alone metadata

The process of automatically extracting document metadata from unstructured text documents. Extracting key entities such as person names, organizations, locations, dates, specialized terms and product terminology from free-form text can empower organizations to not only improve keyword search but also open the door to semantic search, faceted search and document repurposing.

Having the potential to expand in scope, area or size. In the case of the Dublin Core, the ability to extend a core set of meta data with additional elements.

Complex structures formed from relationships that can be used in place of an individual term in a statement.

The extent to which a system is broken down into small parts. For example, the term industry granularity describes the extent to which industry subject headings are broken down into narrower subheadings so that users can navigate to very fine grained terms.

The process of evaluating information entities and creating terms that aid in finding and accessing the entity. Index terms may be in natural language, controlled vocabulary or a classification notation.

The subset of NLP technology that specifically deals with extracting structured information (entity types, relations, events, sentiments) from unstructured text.

Individual objects, entities

A word which occurs in a text more often than we would expect to occur by chance alone. Keywords are calculated by carrying out a statistical test (e.g., log linear) which compares the word frequencies in a text against their expected frequencies derived in a much larger corpus, which acts as a reference for general language use.

The content of a particular field of knowledge, which can include an area of human endeavor, an autonomous computer activity, or other specialized discipline.

The combination of the knowledge model and the linguistic rules for a particular domain that enables deep semantic extraction from content.

A set for all forms of a word that have the same meanings, e.g. run, runs, ran, running. In this case run is the lemma.

The scientific study of language.

A scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.  A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples. Hence the software program must generalize from the given examples, so as to be able to produce a useful output in new cases.

Properties of the object that are defined in a specification, including "author/creator", "title", and subject.

A full set of structured relevant metadata, comprising all relevant elements, describing one object.

 A field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and natural languages. Specifically, the process of a computer extracting meaningful information from natural language input and/or producing natural language output. Modern NLP algorithms are grounded in machine learning, especially statistical machine learning.

A rigorous organization of a knowledge domain that includes multiple types of relationships, including hierarchical, and equivelance, contains all the relevant entities in a domain and describes how they are related. An ontology is the explicit specification of a conceptualization; it captures the structure of a domain.

This process may be divided into two parts: lexical analysis and semantic parsing. Lexical analysis divides strings into components based on punctuation or tagging. Semantic parsing then attempts to determine the meaning of the string.

A variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

The origin, or the source of something, or the history of the ownership or location. The term is used in information retrieval for describing the process of going back to the original source of a piece of data.

A data model for objects ("resources") and relations between them. RDF provides a method for representing the meaning of data which can be embedded in XML syntax.

Each unique fact can be represented as an RDF Triple and includes a subject, predicate and object.

Ways in which classes, objects, individuals and entities can be related.

Formally stated descriptions of what must be true in order for an assertion to be accepted.

Statements, usually in an if-then form, that describes logical inferences.

In general terms, any organization, coding, outline or plan of concepts. In terms of metadata, a systematic, orderly combination of elements or terms. In terms of DCMI term declarations represented in XML or RDF schema language, schemas are machine-processable specifications which define the structure and syntax of metadata specifications in a formal schema language. In terms of an encoding scheme, a set of rules for encoding information that supports a specific community of users.

A set of machine-interpretable representations used to model an area of knowledge or some part of the world, including software. Examples of such models are ontologies that embody some community agreement, logic-based representations, etc. Depending upon the framework or language used for modeling, different terminologies exist for denoting the building blocks of semantic models.

A future-state environment that will automatically connect multiple items of content that have the same conceptual characteristics (semantics). Even when data is not physically tagged with specific metadata, the Semantic Web will provide the coding language that can resort to an ontology which describes all possible relationships in the universe.

The attitude of a speaker or a writer with respect to some topic or the overall tonality of a document. Sentiment analysis refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials.

A query language purpose-built to retrieve RDF Triples. The language was developed by the Worldwide Web Consortium (WC#).

An alphabetical list of words or phrases that represent a concept that is under authority control, e.g. the Library of Congress

The  practice and science of classification. A taxonomic scheme is a particular classification ("the taxonomy of ..."), arranged in a hierarchical structure. Typically this is organized by supertype-subtype relationships, also called generalization-specialization relationships, or less formally, parent-child relationships. In such an inheritance relationship, the subtype by definition has the same properties, behaviors, and constraints as the supertype plus one or more additional properties, behaviors, or constraints. For example: car is a subtype of vehicle, so any car is also a vehicle, but not every vehicle is a car. Therefore a type needs to satisfy more constraints to be a car than to be a vehicle. Another example: any shirt is also a piece of clothing, but not every piece of clothing is a shirt. Hence, a type must satisfy more parameters to be a shirt than to be a piece of clothing.

Time related characteristics of the intellectual content of the resource.

A purpose-built database for the storage and retrieval of Resource Description Framework (RDF) metadata. Much like a relational database, one stores information in a triplestore and retrieves it via a query language. Unlike a relational database, a triplestore is optimized for the storage and retrieval of many short statements called triples, in the form of subject-predicate-object.

A string of characters used to identify a name or a resource on the Internet.

A narrative, which most commonly describes foreseeable interactions of user roles and the technical system. This narrative describes one way that a system is envisaged to be used in the context of an activity in a defined time-frame. The time-frame for a scenario could be a single transaction; a business operation; a day or other period; or the whole operational life of a system. Similarly the scope of a scenario could be a single system or piece of equipment; an equipped team or department; or an entire organization. Scenarios are frequently used as part of the system development process.

An approach to the design and planning of websites which, like architecture itself, involves technical, aesthetic and functional criteria. The focus of website architecture is on the user and on user requirements. This requires particular attention to usability, web content, navigation architecture, information architecture and web design.

Computer language often used to structure documents; it imposes no semantic constraints on the meaning of these documents.