Acronyms & GlossaryAI3:::Adaptive InformationAI3:::Adaptive Information

Glossary

There are terms relevant to artificial intelligence, knowledge bases and semantic technologies. The “official” ones used by Structured Dynamics in its various projects and products are provided in alphabetical order below. Most definitions are from Wikipedia or standards groups, except in those cases where they are terms of art of SD initiatives. Terms in bold are found elsewhere in the glossary.

A – B – C – D – E – F – G – H – I – J – K – L – M – N – O – P – Q – R – S – T – U – V – W – X – Y – Z

Acronyms

Listed at the bottom part of this page are acronyms and definitions related to artificial intelligence, knowledge bases and semantic technologies. Most definitions are from Wikipedia, with the remaining from the appropriate standards group.

A – B – C – D – E – F – G – H – I – J – K – L – M – N – O – P – Q – R – S – T – U – V – W – X – Y – Z

Glossary Listings

A

ABox: An ABox (for assertions, the basis for A in ABox) is an “assertion component”; that is, a fact associated with a terminological vocabulary within a knowledge base. ABox are TBox-compliant statements about instances belonging to the concept of an ontology. Instances and instance records reside within the ABox.
Accuracy: A statistical measure of how well a binary classification test correctly identifies or excludes a condition. It is calculated as the sum of true positives and true negatives divided by the total population.
Adaptive ontology: An adaptive ontology is a conventional knowledge representational ontology that has added to it a number of specific best practices, including modeling the ABox and TBox constructs separately; information that relates specific types to different and appropriate display templates or visualization components; use of preferred labels for user interfaces, as well as alternative labels and hidden labels; defined concepts; and a design that adheres to the open world assumption.
Administrative ontology: Administrative ontologies govern internal application use and user interface interactions.

Annotation: An annotation, specifically as an annotation property, is a way to provide metadata or to describe vocabularies and properties used within an ontology. Annotations do not participate in reasoning or coherency testing for ontologies.
Artificial intelligence: AI is the use of computers to do or assist complex human tasks or reasoning. There are many, broad sub-fields from pattern recognition to robotics and complex planning and optimizations.

Assertion: In RDF and knowledge representation, an assertion is a triple statement that claims truthfulness for a given premise or axiom.

Attributes: These are the aspects, features, characteristics, or descriptors that qualify individual entities. Attributes are the way we describe and characterize individual things. Key-value pairs match an attribute with a value; the value may be a reference to another object, an actual value or a descriptive label or string. In an RDF statement, an attribute is expressed as a property (or predicate or relation). In intensional logic, all attributes or characteristics of similarly classifiable items define the membership in that set.
Attribute type: An aggregation (or class) of multiple attributes that have similar characteristics amongst themselves. As with other types, shared characteristics are subsumed over some essence(s) that give the type its unique character.

Axiom: An axiom is a premise or starting point of reasoning. In an ontology, each statement (assertion) is an axiom.

B

Binding: Binding is the creation of a simple reference to something that is larger and more complicated and used frequently. The simple reference can be used instead of having to repeat the larger thing.
Blank node: Also called a bnode, a blank node in RDF is a resource for which a URI or literal is not given. A blank node indicates the existence of a thing, implied by the structure of the knowledge graph, but which was never explicitly identified by giving it a URI. Blank nodes have no meaning outside of their current graph and therefore can not be mapped to other resources or graphs.

C

Class: A class is a collection of sets or instances (or sometimes other mathematical objects) which can be unambiguously defined by a property that all of its members share. In ontologies, classes may also be known as sets, collections, concepts, types of objects, or kinds of things.
Closed World Assumption: CWA is the presumption that what is not currently known to be true, is false. CWA also has a logical formalization. CWA is the most common logic applied to relational database systems, and is particularly useful for transaction-type systems. In knowledge management, the closed world assumption is used in at least two situations: 1) when the knowledge base is known to be complete (e.g., a corporate database containing records for every employee), and 2) when the knowledge base is known to be incomplete but a “best” definite answer must be derived from incomplete information. See contrast to the open world assumption.
Collection: See class.
Concept: See class.
Cyc: A common-sense knowledge base that has been under development for over 20 years backed by 1000 person-years of effort. The smaller OpenCyc version is available in OWL as open source; a ResearchCyc version of the entire system is available to researchers. The Cyc platform contains its own logic language, CycL, and has many buillt-in functions in areas such as natural language processing, search, inferencing and the like. UMBEL is based on a subset of Cyc.

D

Data Space: A data space may be personal, collective or topical, and is a virtual “container” for related information irrespective of storage location, schema or structure.
Dataset: An aggregation of similar kinds of things or items, mostly comprised of instance records.
DBpedia: A project that extracts structured content from Wikipedia, and then makes that data available as linked data. There are millions of entities characterized by DBpedia in this way. As such, DBpedia is one of the largest — and most central — hubs for linked data on the Web.
DOAP: DOAP (Description Of A Project) is an RDF schema and XML vocabulary to describe open-source projects.
Description logics: Description logics and their semantics traditionally split concepts and their relationships from the different treatment of instances and their attributes and roles, expressed as fact assertions. The concept split is known as the TBox and represents the schema or taxonomy of the domain at hand. The TBox is the structural and intensional component of conceptual relationships. The second split of instances is known as the ABox and describes the attributes of instances (and individuals), the roles between instances, and other assertions about instances regarding their class membership with the TBox concepts.
Distant supervision: A method to use knowledge bases to label entities automatically in text through machine learning, which is then used to extract features and train a machine learning classifier. The knowledge bases provide coherent positive training examples and avoid the high cost and effort of manual labeling.
Domain: The collection of objects and their relationships germane to a particular discourse or scope of inquiry. The domain bounds the scope of a given knowledge representation project. Scoping the domain is one of the first activities undertaken in a new KR project.

Domain ontology: Domain (or content) ontologies embody more of the traditional ontology functions such as information interoperability, inferencing, reasoning and conceptual and knowledge capture of the applicable domain.

E

Entity: The basic, real things in our domain of interest. An entity is an individual object or member of a class; when affixed with a proper name or label is also known as a named entity (thus, named entities are a subset of all entities). Entities are described and characterized by attributes. Entities are connected or related to one another through relations.
Entity–attribute–value model: EAV is a data model to describe entities where the number of attributes (properties, parameters) that can be used to describe them is potentially vast, but the number that will actually apply to a given entity is relatively modest. In the EAV data model, each attribute-value pair is a fact describing an entity. EAV systems trade off simplicity in the physical and logical structure of the data for complexity in their metadata, which, among other things, plays the role that database constraints and referential integrity do in standard database designs.
Entity recognition: The use of natural language processing to identify specific entities in text. Often used in conjunction with named entities, where it is abbreviated NER.
Entity type: Aggregations or collections or classes of similar entities, which also share some essence.
Essence: The attribute or set of attributes that make an entity what it fundamentally is; it is a unique or distinguishing attribute that helps define a type.
Extensional: The extension of a class, concept, idea, or sign consists of the things to which it applies, in contrast with its intension. For example, the extension of the word “dog” is the set of all (past, present and future) dogs in the world. The extension is most akin to the attributes or characteristics of the instances in a set defining its class membership.

F

Fact: A basic statement or assertion within an ontology or knowledge base.
False negative: An error where a test result indicates that a condition failed, while it actually was successful. That is, the test result indicates a negative, when the correct result should have been positive. Also known as a false negative error or Type II error in statistics. It is abbreviated FN.
False positive: An error where a test result indicates that a condition was met or achieved, while it actually should have failed. That is, the test result indicates a positive, when the correct result should have been negative. Also known as a false positive error or Type I error in statistics. It is abbreviated FP.
FOAF: FOAF (Friend of a Friend) is an RDF schema for machine-readable modeling of homepage-like profiles and social networks.
Folksonomy: A folksonomy is a user-generated set of open-ended labels called tags organized in some manner and used to categorize and retrieve Web content such as Web pages, photographs, and Web links.

G

GeoNames: GeoNames integrates geographical data such as names of places in various languages, elevation, population and others from various sources.
GRDDL: GRDDL is a markup format for Gleaning Resource Descriptions from Dialects of Languages; that is, for getting RDF data out of XML and XHTML documents using explicitly associated transformation algorithms, typically represented in XSLT.

H

High-level Subject: A high-level subject is both a subject proxy and category label used in a hierarchical subject classification scheme (taxonomy). Higher-level subjects are classes for more atomic subjects, with the height of the level representing broader or more aggregate classes.

I

Individual: See Instance.
Inferencing: Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The logic within and between statements in an ontology is the basis for inferring new conclusions from it, using software applications known as inference engines or reasoners.
Instance: Instances are the basic, “ground level” components of an ontology. An instance is an individual member of a class, also used synonomously with entity. The instances in an ontology may include concrete objects such as people, animals, tables, automobiles, molecules, and planets, as well as abstract instances such as numbers and words. An instance is also known as an individual, with member and entity also used somewhat interchangeably.
Instance record: An instance with one or more attributes also provided.
irON: irON (instance record and Object Notation) is a abstract notation and associated vocabulary for specifying RDF (Resource Description Framework) triples and schema in non-RDF forms. Its purpose is to allow users and tools in non-RDF formats to stage interoperable datasets using RDF.
Intensional: The intension of a class is what is intended as a definition of what characteristics its members should have; it is akin to a definition of a concept and what is intended for a class to contain. It is therefore like the schema aspects (or TBox) in an ontology.

J

K

Key-value pair: Also known as a name–value pair or attribute–value pair, a key-value pair is a fundamental, open-ended data representation. All or part of the data model may be expressed as a collection of tuples <attribute name, value> where each element is a key-value pair. The key is the defined attribute and the value may be a reference to another object or a literal string or value. In RDF triple terms, the subject is implied in a key-value pair by nature of the instance record at hand.
Kind: Used synonymously herein with class.
Knowledge base: A knowledge base (abbreviated KB or kb) is a special kind of database for knowledge management. A knowledge base provides a means for information to be collected, organized, shared, searched and utilized. Formally, the combination of a TBox and ABox is a knowledge base.
Knowledge graph: See ontology.
Kind: Used synonymously herein with class.
Knowledge representation: A field of artificial intelligence dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks.
Knowledge supervision: A method of machine learning to use knowledge bases in a purposeful way to create features, and negative and positive training sets in order to train the classifiers or extractors. Distant supervision also uses knowledge bases, but not is such a purposeful, directed manner across multiple machine learning problems.

L

Linkage: A specification that relates an object or attribute name to its full URI (as required in the RDF language).
Linked data: Linked data is a set of best practices for publishing and deploying instance and class data using the RDF data model, and uses uniform resource identifiers (URIs) to name the data objects. The approach exposes the data for access via the HTTP protocol, while emphasizing data interconnections, interrelationships and context useful to both humans and machine agents.

M

Machine learning: The construction of algorithms that can learn from and make predictions on data by building a model from example inputs. A wide variety of techniques and algorithms ranging from supervised to unsupervised may be employed.
Mapping: A considered correlation of objects in two different sources to one another, with the relation between the objects defined via a specific property. Linkage is a subset of possible mappings.
Member: Used synonomously herein with instance.
Metadata: Metadata (metacontent) is supplementary data that provides information about one or more aspects of the content at hand such as means of creation, purpose, when created or modified, author or provenance, where located, topic or subject matter, standards used, or other annotation characteristics. It is “data about data”, or the means by which data objects or aggregations can be described. Contrasted to an attribute, which is an individual characteristic intrinsic to a data object or instance, metadata is a description about that data, such as how or when created or by whom.
Metamodeling: Metamodeling is the analysis, construction and development of the frames, rules, constraints, models and theories applicable and useful for modeling a predefined class of problems.
Microdata: Microdata is a proposed specification used to nest semantics within existing content on web pages. Microdata is an attempt to provide a simpler way of annotating HTML elements with machine-readable tags than the similar approaches of using RDFa or microformats.
Microformats: A microformat (sometimes abbreviated μF or uF) is a piece of mark up that allows expression of semantics in an HTML (or XHTML) web page. Programs can extract meaning from a web page that is marked up with one or more microformats.

N

Natural language processing: NLP is the process of a computer extracting meaningful information from natural language input and/or producing natural language output. NLP is one method for assigning structured data characterizations to text content for use in semantic technologies. (Hand assignment is another method.) Some of the specific NLP techniques and applications relevant to semantic technologies include automatic summarization, coreference resolution, machine translation, named entity recognition (NER), question answering, relationship extraction, topic segmentation and recognition, word segmentation, and word sense disambiguation, among others.
Named entity: See entity.
Named entity recognition: See entity recognition; also called NER.

O

OBIE: Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. Ontology-based information extraction (OBIE) is the use of an ontology to inform a “tagger” or information extraction program when doing natural language processing. Input ontologies thus become the basis for generating metadata tags when tagging text or documents.
Object: An object is anything we can think about or talk about. In their use in semantic technologies, objects are nouns and always given a URI (bnodes can also act in the object position but they lack a persistent URI).
Ontology: An ontology is a data model that represents a set of concepts within a domain and the relationships between those concepts. Loosely defined, ontologies on the Web can have a broad range of formalism, or expressiveness or reasoning power.
Ontology-driven application: Ontology-driven applications (or ODapps) are modular, generic software applications designed to operate in accordance with the specifications contained in one or more ontologies. The relationships and structure of the information driving these applications are based on the standard functions and roles of ontologies (namely as domain ontologies), as supplemented by UI and instruction sets and validations and rules.
Open Semantic Framework: The open semantic framework, or OSF, is a combination of a layered architecture and an open-source, modular software stack. The stack combines many leading third-party software packages with open source semantic technology developments from Structured Dynamics.
Open World Assumption: OWA is a formal logic assumption that the truth-value of a statement is independent of whether or not it is known by any single observer or agent to be true. OWA is used in knowledge representation to codify the informal notion that in general no single agent or observer has complete knowledge, and therefore cannot make the closed world assumption. The OWA limits the kinds of inference and deductions an agent can make to those that follow from statements that are known to the agent to be true. OWA is useful when we represent knowledge within a system as we discover it, and where we cannot guarantee that we have discovered or will discover complete information. In the OWA, statements about knowledge that are not included in or inferred from the knowledge explicitly recorded in the system may be considered unknown, rather than wrong or false. Semantic Web languages such as OWL make the open world assumption. See contrast to the closed world assumption.
OPML: OPML (Outline Processor Markup Language) is an XML format for outlines, and is commonly used to exchange lists of web feeds between web feed aggregators.
OWL: The Web Ontology Language (OWL) is designed for defining and instantiating formal Web ontologies. An OWL ontology may include descriptions of classes, along with their related properties and instances. There are also a variety of OWL dialects.

P

Precision: The fraction of retrieved documents that are relevant to the query. It is measured as true positives divided by all measured positives (true and false). High precision indicates a high percentage of true positives in relation to all positive results.
Predicate: See Property.
Property: Properties are the ways in which classes and instances can be related to one another. Between objects, properties are thus a relationship, and are also known as predicates. Properties are used to define an attribute or relation for an instance.
Punning: In computer science, punning refers to a programming technique that subverts or circumvents the type system of a programming language, by allowing a value of a certain type to be manipulated as a value of a different type. When used for ontologies, it means to treat a thing as both a class and an instance, with the use depending on context.

Q

R

RDF: Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata model but which has come to be used as a general method of modeling information, through a variety of syntax formats. The RDF metadata model is based upon the idea of making statements about resources in the form of subject-predicate-object expressions, called triples in RDF terminology. The subject denotes the resource, and the predicate denotes traits or aspects of the resource and expresses a relationship between the subject and the object.
RDFa: RDFa uses attributes from meta and link elements, and generalizes them so that they are usable on all elements allowing annotation markup with semantics. RDFa 1.1 is a W3C Recommendation that removes prior dependence on the XML namespace and expands HTML5 and SVG support, among other changes.
RDF Schema: RDFS or RDF Schema is an extensible knowledge representation language, providing basic elements for the description of ontologies, otherwise called RDF vocabularies, intended to structure RDF resources.
Reasoner: A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is a piece of software able to infer logical consequences from a set of asserted facts or axioms. The notion of a semantic reasoner generalizes that of an inference engine, by providing a richer set of mechanisms.
Reasoning: Reasoning is one of many logical tests using inference rules as commonly specified by means of an ontology language, and often a description language. Many reasoners use first-order predicate logic to perform reasoning; inference commonly proceeds by forward chaining or backward chaining.
Recall: The fraction of the documents that are relevant to the query that are successfully retrieved. It is measured as true positives divided by all potential positives that could be returned from the corpus. High recall indicates a high yield in obtaining relevant results.
Record: As used herein, a shorthand reference to an instance record.
Reference concept: Any of the noun objects within UMBEL, and abbreviated as RC. An RC may be either an entity, entity type, attribute, attribute type, relation, relation type, topic or abstract concept. There are presently about 35 K RCs in UMBEL. All RCs are objects.
Relation: A connection between any two objects. Relations are properties.
Relation type: An aggregation (or class) of multiple relations that have similar characteristics amongst themselves. As with other types, shared characteristics are subsumed over some essence(s) that give the type its unique character.
RSS: RSS (an acronym for Really Simple Syndication) is a family of web feed formats used to publish frequently updated digital content, such as blogs, news feeds or podcasts.

S

schema.org: Schema.org is an initiative launched by the major search engines of Bing, Google and Yahoo!, and later jointed by Yandex, in order to create and support a common set of schema for structured data markup on web pages. schema.org provided a starter set of schema and extension mechanisms for adding to them. schema.org supports markup in microdata, microformat and RDFa formats.
Semantic enterprise: An organization that uses semantic technologies and the languages and standards of the semantic Web, including RDF, RDFS, OWL, SPARQL and others to integrate existing information assets, using the best practices of linked data and the open world assumption, and targeting knowledge management applications.
Semantic technology: Semantic technologies are a combination of software and semantic specifications that encode meanings separately from data and content files and separately from application code. This approach enables machines as well as people to understand, share and reason with data and specifications separately. With semantic technologies, adding, changing and implementing new relationships or interconnecting programs in a different way can be as simple as changing the external model that these programs share. New data can also be brought into the system and visualized or worked upon based on the existing schema. Semantic technologies provide an abstraction layer above existing IT technologies that enables bridging and interconnection of data, content, and processes.
Semantic Web: The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web of unstructured documents into a “web of data”. It builds on the W3C’s Resource Description Framework (RDF).
Semset: A semset is the use of a series of alternate labels and terms to describe a concept or entity. These alternatives include true synonyms, but may also be more expansive and include jargon, slang, acronyms or alternative terms that usage suggests refers to the same concept.
SIOC: Semantically-Interlinked Online Communities Project (SIOC) is based on RDF and is an ontology defined using RDFS for interconnecting discussion methods such as blogs, forums and mailing lists to each other.
SKOS: SKOS or Simple Knowledge Organisation System is a family of formal languages designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary; it is built upon RDF and RDFS.
SKSI: Semantic Knowledge Source Integration provides a declarative mapping language and API between external sources of structured knowledge and the Cyc knowledge base.
SPARQL: SPARQL (pronounced “sparkle”) is an RDF query language; its name is a recursive acronym that stands for SPARQL Protocol and RDF Query Language.
Statement: A statement is a “triple” in an ontology, which consists of a subject – predicate – object (S-P-O) assertion. By definition, each statement is a “fact” or axiom within an ontology.
Subject: A subject is always a noun or compound noun and is a reference or definition to a particular object, thing or topic, or groups of such items. Subjects are also often referred to as concepts or topics.
Subject extraction: Subject extraction is an automatic process for retrieving and selecting subject names from existing knowledge bases or data sets. Extraction methods involve parsing and tokenization, and then generally the application of one or more information extraction techniques or algorithms.
Subject proxy: A subject proxy as a canonical name or label for a particular object; other terms or controlled vocabularies may be mapped to this label to assist disambiguation. A subject proxy is always representative of its object but is not the object itself.
SuperType: One of about 30 segregated splits within UMBEL that are mostly disjoint from one another and mostly conform to broad groupings of entities. SuperTypes are a major organizational dimension of UMBEL.
Supervised learning: A machine learning task of inferring a function from labeled training data, which optimally consists of positive and negative training sets. The supervised learning algorithm analyzes the training data and produces an inferred function to correctly determine the class labels for unseen instances.

T

Tag: A tag is a keyword or term associated with or assigned to a piece of information (e.g., a picture, article, or video clip), thus describing the item and enabling keyword-based classification of information. Tags are usually chosen informally by either the creator or consumer of the item.
TBox: A TBox (for terminological knowledge, the basis for T in TBox) is a “terminological component”; that is, a conceptualization associated with a set of facts. TBox statements describe a conceptualization, a set of concepts and properties for these concepts. The TBox is sufficient to describe an ontology (best practice often suggests keeping a split between instance records — and ABox — and the TBox schema).
Taxonomy: In the context of knowledge systems, taxonomy is the hierarchical classification of entities of interest of an enterprise, organization or administration, used to classify documents, digital assets and other information. Taxonomies can cover virtually any type of physical or conceptual entities (products, processes, knowledge fields, human groups, etc.) at any level of granularity.
Topic: The topic (or theme) is the part of the proposition that is being talked about (predicated). In topic maps, the topic may represent any concept, from people, countries, and organizations to software modules, individual files, and events. Topics and subjects are closely related.
Topic Map: Topic maps are an ISO standard for the representation and interchange of knowledge. A topic map represents information using topics, associations (similar to a predicate relationship), and occurrences (which represent relationships between topics and information resources relevant to them), quite similar in concept to the RDF triple.
Training set: A set of data used to discover potentially predictive relationships. In supervised learning, a positive training set provides data that meets the training objectives; a negative training set fails to meet the objectives.
Triple: A basic statement in the RDF language, which is comprised of a subject – property – object construct, with the subject and property (and object optionally) referenced by URIs.
True negative: A correct result, but one which fails (is negative) to meet the test objective. It is abbreviated TN.
True positive: A correct result, and one which succeeds (is positive) to meet the test objective. It is abbreviated TP.
Type: Used synonymously herein with class. However, it is important to recognize the type-token distinction in usage.
Typology: Is a flat, hierarchical taxonomy comprised of related entity types within the context of a given UMBEL SuperType (ST). Typologies are a critical connection point between the TBox and ABox. The link shown here uses an archaeology example.

U

UMBEL: UMBEL, short for Upper Mapping and Binding Exchange Layer, is an upper ontology of about 35,000 reference concepts, designed to provide common mapping points for relating different ontologies or schema to one another, and a vocabulary for aiding that ontology mapping, including expressions of likelihood relationships distinct from exact identity or equivalence. This vocabulary is also designed for interoperable domain ontologies.
Unsupervised learning: A form of machine learning, this approach attempts to find meaningful, hidden patterns in unlabeled data.
Upper ontology: An upper ontology (also known as a top-level ontology or foundation ontology) is an ontology that describes very general concepts that are the same across all knowledge domains. An important function of an upper ontology is to support very broad semantic interoperability between a large number of ontologies that are accessible ranking “under” this upper ontology.

V

Vocabulary: A vocabulary in the sense of knowledge systems or ontologies are controlled vocabularies. They provide a way to organize knowledge for subsequent retrieval. They are used in subject indexing schemes, subject headings, thesauri, taxonomies and other form of knowledge organization systems.

W

Wikidata: This is a crowdsourced, open knowledge base of (currently) about 18 million structured entity records. Each record consists of attributes and values with robust cross-links to multiple languages. Wikidata is a key entities source.
Wikipedia: Wikipedia is a crowdsourced, free-access and free-content knowledge base of human knowledge. It has nearly 5 million articles in its English version. Across all Wikipedias there are nearly 35 million articles in 288 different language versions.
WordNet: WordNet is a lexical database for the English language. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. The purpose is twofold: to produce a combination of dictionary and thesaurus that is more intuitively usable, and to support automatic text analysis and artificial intelligence applications. The database and software tools can be downloaded and used freely. Multiple language versions exist, and WordNet is a frequent reference structure for semantic applications.

X

Y

YAGO: “Yet another great ontology” is a WordNet structure placed on top of Wikipedia.

Z

Acronyms Listings

A – B – C – D – E – F – G – H – I – J – K – L – M – N – O – P – Q – R – S – T – U – V – W – X – Y – Z

A

ADO: ActiveX Data Objects
AI: Artificial Intelligence
Ajax: Asynchronous JavaScript and XML
ANSI: American National Standards Institute
ANT: Another Neat Tool
API: Application Programming Interface
ARPA: Advanced Research Projects Agency (see also DARPA)
ARPANET: Advanced Research Projects Agency Network
ASCII: American Standard Code for Information Interchange
ASG: Abstract Semantic Graph
ASN.1: Abstract Syntax Notation 1
ASP: Application Service Provider

ASP (MS ASP): Active Server Pages
AST: Abstract Syntax Tree
AWT: Abstract Windowing Toolkit

B

B2B: Business-to-Business
B2C: Business-to-Consumer
Blog: Web Log
BPDM: Business Process Definition Metamodel
BPEL: Business Process Execution Language
BPEL4WS: BPEL for Web Services
BPM: Business Process Management
BPMl: Business Process Management Language
BPMN: Business Process Modeling Notation

C

CAPTCHA: Completely Automated Public Turing Test to tell Computers and Humans Apart
CASE: Computer-aided Software Engineering
CDF: Common Data Format
CIFS: Common Internet Filesystem
CIM: Common Information Model
CJK: Chinese, Japanese, and Korean
CJKV: Chinese, Japanese, Korean, and Vietnamese
CMS: Content Management System
CN: Canonical Name
COM: Component Object Model
CORBA: Common Object Request Broker Architecture
COTS: Commercial Off-The-Shelf
CRM: Customer Relationship Management
CS: Computer Science
CSE: Computer Science and Engineering
CSS: Cascading Style Sheets
CSV: Comma-Separated Values
Cyc: Comprehensive, enCYClopedic knowledge base; also OpenCyc and ResearchCyc

D

DAO: Data Access Objects
DAP: Directory Access Protocol
DARPA: Defense Advanced Research Projects Agency
DB: Database
DBA: Database Administrator
DBMS: Database Management System
DCMI: Dublin Core Metadata Initiative
DCOM: Distributed Component Object Model
DDE: Dynamic Data Exchange
DDL: Data Definition Language
DERI: Digital Enterprise Research Institute
DHCP: Dynamic Host Configuration Protocol
DHTML: Dynamic HTML
DITA: Darwin Information Typing Architecture
DLL: Dynamic Link Library
DML: Data Manipulation Language
DNS: Domain Name System
DOAP: Description of a Project
DOM: Document Object Model
DQM: Deep Query Manager
DRM: Digital Rights Management
DSDL: Document Schema Definition Languages
DSM: Dependency Structure Matrix
DSSSL: Document Style Semantics and Specification Language
DTD: Document Type Definition

E

EAI: Enterprise Application Integration
EAP: Extensible Authentication Protocol
EBCDIC: Extended Binary Coded Decimal Interchange Code
EBIR: Extended Boolean Information Retrieval (see IR)
EBML: Extensible Binary Meta Language
ebXML: e-business XML, or electronic messaging for business transactions
EDI: Electronic Data Interchange
EFF: Electronic Frontier Foundation
EISA: Extended Industry Standard Architecture
EJB: Enterprise JavaBean
EMACS: Editor Macros
EOF: End of File
EOL: End of Life
EOL: End of Line
EOM: End of Message
ERP: Enterprise Resource Planning
ETL: Extract, Transform, Load
EULA: End User License Agreement

F

FAQ: Frequently Asked Questions
FEA: Federal Enterprise Architecture
FLOSS: Free/Libre/Open Source Software
FOAF: Friend of a Friend
FOL: First-order Logic
FOLDOC: Free On-line Dictionary of Computing
FOSI: Formatted Output Specification Instance
FOSS: Free and Open Source Software
FSF: Free Software Foundation
FSM: Finite State Machine
FTP: File Transfer Protocol

G

G11N: Globalization
GFDL: GNU Free Documentation License
GIF: Graphics Interchange Format
GIGO: Garbage In, Garbage Out
GIS: Geographic Information System
GMT: Greenwich Mean Time
GNOME: GNU Network Object Model Environment
GNU: GNU’s Not Unix
GPL: General Public License
GRDDL: Gleaning Resource Descriptions from Dialects of Languages
GUI: Graphical User Interface, often pronounced “gooey”

H

HCI: Human Computer Interaction
HDF: Hierarchical Data Format
HPC: High-Performance Computing
HPFS: High Performance File System
HTTP: HyperText Transfer Protocol
HTML: HyperText Markup Language
HTTPd: Hypertext Transport Protocol Daemon

I

I/O: Input/Output
I18N: Internationalization
IANA: Internet Assigned Numbers Authority
ICANN: Internet Corporation for Assigned Names and Numbers
ICMP: Internet Control Message Protocol
ICP: Internet Cache Protocol
IDE: Integrated Development Environment
IE: Internet Explorer
IEEE: Institute of Electrical and Electronics Engineers
IETF: Internet Engineering Task Force
IGMP: Internet Group Management Protocol
IIOP: Internet Inter-Orb Protocol
IIS: Internet Information Services
IM: Instant Messaging
IMAP: Internet Message Access Protocol
IME: Input Method Editor
IP: Intellectual Property
IP: Internet Protocol
IPC: Inter-Process Communication
IPP: Internet Printing Protocol
IPsec: Internet Protocol security
IPX: Internetwork Packet Exchange
IR: Information Retrieval
IRC: Internet Relay Chat
IS: Information Systems
ISA: Industry Standard Architechture
ISO: International Organization for Standardization
ISP: Internet Service Provider
IT: Information Technology

J

J2EE: Java 2 Enterprise Edition
J2ME: Java 2 Micro Edition
J2SE: Java 2 Standard Edition
JAR: Java ARchive
JAXB: Java XML Binding
JAXP: Java API for XML Processing
JAX-RPC: Java XML for Remote Procedure Calls
JCP: Java Community Process
JDBC: Java Database Connectivity
JDK: Java Development Kit
JDOM: Java Document Object Model
JFC: Java Foundation Classes
JINI: Jini Is Not Initials
JMS: Java Message Service
JMX: Java Management Extensions
JNDI: Java Naming and Directory Interface
JNI: Java Native Interface
JRE: Java Runtime Environment
JS: JavaScript
JSF: Java Server Faces
JSON: JavaScript Object Notation
JSP: JavaServer Pages
JSR: Java Specification Requests
JSTL: JavaServer Pages Standard Tag Library
JVM: Java Virtual Machine

K

KB: Kilobyte
KIF: Knowledge Interchange Format
KIM: Knolwledge and Information Management
KB: Knowledge Base
KM: Knowledge Management

L

L10N: Localization
LAMP: Linux Apache MySQL (Perl, PHP, or Python)
LAN: Local Area Network
LDAP: Lightweight Directory Access Protocol
LGPL: [GNU] Lesser General Public License
LIFO: Last In First Out
LSI: Large-Scale Integration
LSI: Latent Semantic Indexing
LZW: Lempel-Ziv-Welch

M

MAPI: Messaging Application Programming Interface
MDA: Model-Driven Architecture
MDI: Multiple Document Interface
MeSH: Medical Subject Headings
MFC: Microsoft Foundation Classes
MIME: Multipurpose Internet Mail Extensions
MIS: Management Information Systems
MOF: Meta Object Facility
MPL: Mozilla Public License
MSDN: Microsoft Developer Network
MSI: Medium-Scale Integration
MT: Machine Translation
MVC: Model-View-Controller

N

N3: Notation 3, an RDF (non-XML) notation
NAS: Network-Attached Storage
NFS: Network Filesystem
NIC: Network Interface Card
NIST: National Institute of Standards and Technology
NLP: Natural Language Processing
NRN: No Reply Necessary
NSA: National Security Agency

O

OASIS: Organization for the Advancement of Structured Information Standards
ODBC: Open Database Connectivity
OIL: Ontology Inference Layer or Ontology Interchange Language
OLAP: Online Analytical Processing
OLTP: Online Transaction Processing
OMG: Object Management Group
OO: Object-Oriented
OOP: Object-Oriented Programming
OPML: Outline Processor Markup Language
ORB: Object Request Broker
OS: Open Source
OSDN: Open Source Developer Network
OSI: Open Source Initiative
OSI Model: Open Systems Interconnection Model
OSS: Open-Source Software
OSS: Operational Support Systems
OSTG: Open Source Technology Group (formerly OSDN)
OWL: Web Ontology Language; also OWL Full, OWL DL and OWL Lite

P

P2P: Peer-To-Peer
PDF: Portable Document Format
PERL: Practical Extraction and Reporting Language
PHP: PHP: Hypertext Preprocessor
PNG: Portable Network Graphics
PnP: Plug-and-Play
POP3: Post Office Protocol v3
PS: PostScript
PURL: Persistent Uniform Resource Locator

Q

QA: Quality Assurance

R

RAD: Rapid Application Development
RAID: Redundant Array of Inexpensive Disks
RC: Release Candidate
RDBMS: Relational Database Management System
RDF: Resource Description Framework
RDFa: RDF extensions (also RDF/A)
RDFS: RDF Schema, also shown as RDF-S or RDF/S
regex: Regular Expression
REST: Representational State Transfer
RFC: Request For Comments
RGB: Red, Green, Blue
RIA: Rich Internet Application
RLE: Run-Length Encoding
RMI: Remote Method Invocation
RPC: Remote Procedure Call
RSS: Rich Site Summary, RDF Site Summary, or Really Simple Syndication
RTFM: Read The @!%*! Manual
RTL: Right-to-Left
RuleML: Rule Markup Language

S

SAN: Storage Area Network
SAX: Simple API for XML
SCSI: Small Computer System Interface
SDI: Single Document Interface
SDIO: Secure Digital Input Output
SDK: Software Development Kit
SEAL: Semantics-directed Environment Adaptation Language
SEF: Search Engine Friendly
SGML: Standard Generalized Markup Language
SIOC: Semantically Interlinked Online Communities
SKOS: Simple Knowledge Organization System
SMTP: Simple Mail Transfer Protocol
SNA: Systems Network Architecture
SOA: Service-Oriented Architecture
SOAP: Simple Object Access Protocol
SPARQL: recursively, SPARQL Protocol and RDF Query Language
SQL: Structured Query Language
SSL: Secure Socket Layer
SWISHer: Semantic Web, Interoperability, Standards, HTML Experts Reference
SWRL: Semantic Web Rule Language
SWSL: Semantic Web Services Language
SWSO: Semantic Web Services Ontology
SWT: Stardard Widget Toolkit

T

TB: Terabyte
TCP/IP: Transmission Control Protocol/Internet Protocol
TCP: Transmission Control Protocol
tf-idf: term frequency/inverse document frequency (see IR)
TIX: Tactical Internet eXploitation
TLA: Three Letter Acronym

U

UCS: Universal Character Set
UDDI: Universal Description, Discovery, and Integration
UI: User Interface
UIMA: Unstructured Information Management Architecture
UML: Unified Modeling Language
UML: User-Mode Linux
UNC: Universal Naming Convention
UPS: Uninterruptible Power Supply
URI: Uniform Resource Identifier
URL: Uniform Resource Locator
URN: Uniform Resource Name
USML: UDDI Search Markup Language
UTC: Coordinated Universal Time
UTF: Unicode Transformation Format / UTF-8 / UTF-16 / UTF-32
UUID: Universally Unique Identifier

V

VLSI: Very-Large-Scale Integration
VM: Virtual Machine
VM: Virtual Memory
VPN: Virtual Private Network
VSAM: Virtual Storage Access Method
VSM: Vector Space Model (see IR)

W

W3C: World Wide Web Consortium
WAFS: Wide Area File Services
WAI: Web Accessibility Initiative
WAIS: Wide Area Information Server
WAN: Wide Area Network
WAR: Web ARchive File
WBEM: Web-Based Enterprise Management
WCAG: Web Content Accessibility Guidelines
WebDAV: WWW Distributed Authoring and Versioning
WfMC: Workflow Management Coalition
WS-BPEL: Web Services BPEL (see BPEL4WS)
WSCL: Web Services Conversation Language
WSCM: Web Services Component Model
WSDL: Web Services Description Language
WSEL: Web Services Endpoint Language
WSFL: Web Services Flow Language
WSIA: Web Services for Interactive Applications
WS-Inspection: Web Services Inspection Language
WSMF: Web Services Management Framework
WSML: Web Services Meta Language
WSMO: Web Service Modeling Ontology
WSMT: Web Services Modeling Toolkit
WSMX: Web Service Execution Environment
WSUI: Web Services User Interface
WSXL: Web Services Experience Language
WWW: World Wide Web
WYSIWYG: What You See Is What You Get

X

XAG: XML Accessibility Guidelines
XAML: eXtensible Application Markup Language
XCBL: XML Common Business Library
XHTML: eXtensible Hypertext Markup Language
XKMS: XML Key Managment Specification
XLANG: Web Services for Business Process Design
XLink: XML Linking language
XMI: XML Metadata Interchange
XML: eXtensible Markup Language
XPath: XML Path Language
XMPP: eXtensible Messaging and Presence Protocol
XSD: XML Schema Definition
XSDM: eXtensible Semantic Data Model
XPDL: eXtensible Process Definition Language
XQuery: XML Query language
XSL: eXtensible Stylesheet Language
XSL-FO: eXtensible Stylesheet Language Formatting Objects
XSLT: XSL Transformations
XTF: Extensible Tag Framework
XUL: XML-based User-interface Language

Y

YAML: YAML Ain’t Markup Language

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