2005-09-29 testing 2004-03-26 Concept An abstract idea or notion; a unit of thought. Concept Scheme 2005-10-14 A set of concepts, optionally including statements about semantic relationships between those concepts. A concept scheme may be defined to include concepts from different sources. Thesauri, classification schemes, subject heading lists, taxonomies, 'folksonomies', and other types of controlled vocabulary are all examples of concept schemes. Concept schemes are also embedded in glossaries and terminologies. 2004-03-26 testing 2005-09-29 Collection A meaningful collection of concepts. unstable Labelled collections can be used with collectable semantic relation properties e.g. skos:narrower, where you would like a set of concepts to be displayed under a 'node label' in the hierarchy. 2004-10-20 A property which can be used with a skos:Collection. Collectable Property The following rule applies for this property: [(?x ?p ?c) (?c skos:member ?y) (?p rdf:type skos:CollectableProperty) implies (?x ?p ?y)] 2005-09-29 unstable 2004-10-20 2005-09-29 Ordered collections can be used with collectable semantic relation properties, where you would like a set of concepts to be displayed in a specific order, and optionally under a 'node label'. Ordered Collection 2004-10-20 An ordered collection of concepts, where both the grouping and the ordering are meaningful. unstable 2005-09-29 unstable hidden label 2004-12-15 A lexical label for a resource that should be hidden when generating visual displays of the resource, but should still be accessible to free text search operations. The following rule applies for this property: [(?c skos:memberList ?l) elementOfList(?e,?l) implies (?c skos:member ?e)] member list An RDF list containing the members of an ordered collection. 2005-09-29 2004-10-20 unstable A concept that is more general in meaning. testing 2005-09-29 2004-03-26 Broader concepts are typically rendered as parents in a concept hierarchy (tree). has broader Recommended best practice is to reference the resource by means of a string or number conforming to a formal identification system. 1999-07-02 2002-10-04 A reference to a related resource. Relation An image that is a symbolic label for the resource. 2005-10-06 unstable symbolic label This property is roughly analagous to rdfs:label, but for labelling resources with images that have retrievable representations, rather than RDF literals. example 2005-10-05 2004-03-26 testing An example of the use of a concept. Type includes terms describing general categories, functions, genres, or aggregation levels for content. Recommended best practice is to select a value from a controlled vocabulary (for example, the DCMI Type Vocabulary [DCMITYPE]). To describe the physical or digital manifestation of the resource, use the Format element. 1999-07-02 Resource Type The nature or genre of the content of the resource. 2002-10-04 2005-10-05 definition testing A statement or formal explanation of the meaning of a concept. 2004-03-26 2004-10-22 unstable A resource may have only one primary subject per concept scheme. has primary subject A concept that is the primary subject of the resource. 2005-09-29 A general note, for any purpose. 2005-10-05 This property may be used directly, or as a super-property for more specific note types. unstable note This property replaces the two deprecated properties skos:privateNote and skos:publicNote. To describe a note for a particular audience (e.g. 'editor', 'indexer', 'general user') use a note property with a related resource description and the dc:audience property. Narrower concepts are typically rendered as children in a concept hierarchy (tree). A concept that is more specific in meaning. has narrower 2005-09-29 testing 2004-03-26 2004-03-26 2005-09-29 An alternative lexical label for a resource. testing alternative label Acronyms, abbreviations, spelling variants, and irregular plural/singular forms may be included among the alternative labels for a concept. Mis-spelled terms are normally included as hidden labels (see skos:hiddenLabel). Title 2002-10-04 A name given to the resource. 1999-07-02 Typically, a Title will be a name by which the resource is formally known. An entity primarily responsible for making the content of the resource. Creator Examples of a Creator include a person, an organisation, or a service. Typically, the name of a Creator should be used to indicate the entity. 1999-07-02 2002-10-04 A note for an editor, translator or maintainer of the vocabulary. editorial note unstable 2005-10-05 2004-10-21 Description may include but is not limited to: an abstract, table of contents, reference to a graphical representation of content or a free-text account of the content. 2002-10-04 1999-07-02 Description An account of the content of the resource. An entity responsible for making the resource available Examples of a Publisher include a person, an organisation, or a service. Typically, the name of a Publisher should be used to indicate the entity. 1999-07-02 Publisher 2002-10-04 2004-10-21 unstable 2005-10-05 A note about a modification to a concept. change note testing 2005-09-29 A concept with which there is an associative semantic relationship. related to 2004-03-26 unstable 2004-11-11 A subject indicator for a concept. [The notion of 'subject indicator' is defined here with reference to the latest definition endorsed by the OASIS Published Subjects Technical Committee.] This property allows subject indicators to be used for concept identification in place of or in addition to directly assigned URIs. subject indicator 2005-09-29 2004-10-22 2005-09-29 is primary subject of unstable A resource for which the concept is the primary subject. 2005-10-05 A note about the past state/use/meaning of a concept. 2004-10-21 history note unstable 2004-03-26 alternative symbolic label An alternative symbolic label for a resource. testing 2005-10-06 2004-10-20 2005-09-29 A member of a collection. unstable member semantic relation 2005-09-29 2004-03-26 This property should not be used directly, but as a super-property for all properties denoting a relationship of meaning between concepts. A concept related by meaning. testing The preferred lexical label for a resource, in a given language. testing No two concepts in the same concept scheme may have the same value for skos:prefLabel in a given language. 2005-09-29 2004-03-26 preferred label Typically, Date will be associated with the creation or availability of the resource. Recommended best practice for encoding the date value is defined in a profile of ISO 8601 [W3CDTF] and follows the YYYY-MM-DD format. Date 2002-10-04 A date associated with an event in the life cycle of the resource. 1999-07-02 A note that helps to clarify the meaning of a concept. 2005-10-05 testing scope note 2004-03-26 Examples of a Contributor include a person, an organisation, or a service. Typically, the name of a Contributor should be used to indicate the entity. An entity responsible for making contributions to the content of the resource. 1999-07-02 2002-10-04 Contributor 1999-07-02 The present resource may be derived from the Source resource in whole or in part. Recommended best practice is to reference the resource by means of a string or number conforming to a formal identification system. A reference to a resource from which the present resource is derived. 2002-10-04 Source 2004-10-22 A resource for which the concept is a subject. is subject of 2005-09-29 unstable 2004-08-19 This property replaces the deprecated skos:TopConcept class. testing has top concept 2005-09-29 A top level concept in the concept scheme. Coverage Coverage will typically include spatial location (a place name or geographic coordinates), temporal period (a period label, date, or date range) or jurisdiction (such as a named administrative entity). Recommended best practice is to select a value from a controlled vocabulary (for example, the Thesaurus of Geographic Names [TGN]) and that, where appropriate, named places or time periods be used in preference to numeric identifiers such as sets of coordinates or date ranges. 1999-07-02 2002-10-04 The extent or scope of the content of the resource. A concept may be a member of more than one concept scheme. 2005-09-29 in scheme A concept scheme in which the concept is included. testing 2004-03-26 has subject A concept that is a subject of the resource. unstable 2004-10-22 2005-09-29 The following rule may be applied for this property: [(?d skos:subject ?x)(?x skos:broader ?y) implies (?d skos:subject ?y)] 2002-10-04 Subject and Keywords 1999-07-02 The topic of the content of the resource. Typically, a Subject will be expressed as keywords, key phrases or classification codes that describe a topic of the resource. Recommended best practice is to select a value from a controlled vocabulary or formal classification scheme. 2002-10-04 1999-07-02 Recommended best practice is to use RFC 3066 [RFC3066], which, in conjunction with ISO 639 [ISO639], defines two- and three-letter primary language tags with optional subtags. Examples include "en" or "eng" for English, "akk" for Akkadian, and "en-GB" for English used in the United Kingdom. Language A language of the intellectual content of the resource. An unambiguous reference to the resource within a given context. 2002-10-04 Resource Identifier Recommended best practice is to identify the resource by means of a string or number conforming to a formal identification system. Example formal identification systems include the Uniform Resource Identifier (URI) (including the Uniform Resource Locator (URL)), the Digital Object Identifier (DOI) and the International Standard Book Number (ISBN). 1999-07-02 2002-10-04 Typically, a Rights element will contain a rights management statement for the resource, or reference a service providing such information. Rights information often encompasses Intellectual Property Rights (IPR), Copyright, and various Property Rights. If the Rights element is absent, no assumptions can be made about the status of these and other rights with respect to the resource. Rights Management Information about rights held in and over the resource. 1999-07-02 Format The physical or digital manifestation of the resource. 1999-07-02 Typically, Format may include the media-type or dimensions of the resource. Format may be used to determine the software, hardware or other equipment needed to display or operate the resource. Examples of dimensions include size and duration. Recommended best practice is to select a value from a controlled vocabulary (for example, the list of Internet Media Types [MIME] defining computer media formats). 2002-10-04 2004-03-26 No two concepts in the same concept scheme may have the same value for skos:prefSymbol. testing The preferred symbolic label for a resource. 2005-10-06 preferred symbolic label The Semantic Web V.Giurdas Publications Nick Bassiliades Artificial Intelligence March 2006 Fotis Kokkoras Petros Kefalas Ilias Sakellariou Ioannis Vlahavas AI Agent Systems The Semantic Web Architecture Document Encoding & Addressing Protocols & Communication Languages Multi-agent Systems Configuration Knowledge-based Systems Applications Configuration Methods Suggestion & Revision Temporal Logic Applications Temporal Logic Time Representation Knowledge Representation & Reasoning Document Content Representation Definitions & Characteristics of Agents Intelligent Agents Agent Definitions CTL Computational Tree Logic Supervised Learning Machine Learning Machine Learning & Knowledge Discovery Classification/Decision Trees Inference Inference Systems Rule-based Systems Semantic Web Technologies OWL - The Web Ontology Language Problem Solving Constraint Satisfaction Combining Search & Consistency Algorithms Search Algorithms Problem Representation & Search Search Algorithm Selection Process Beam Search BS Heuristic Search Hill-Climbing Search HC Finding the Maximum in a One-Variable Function Genetic Algorithms Problem Solving with Genetic Algorithms Interaction Protocols Action Representation Problem Representation - The STRIPS Model Basic Planning Principles & Techniques Planning Instance Based Learning Bidirectional Associative Memories Neural Networks Associative Memories Agent Characteristics Unsupervised Learning Association Rules Consistency Check Algorithms Mobile Agents Basic Notions STRIPS Principles The QSIM System Advanced Reasoning Knowledge-based Systems Qualitative Reasoning Linear Associators Probability Theory Principles Uncertainty Other Advanced Planning Techniques Advanced Planning Techniques Classification Rule Learning Clusters Classification Methods Classification Applications of Knowledge-based Systems Characteristics, Architecture & Functionality of Knowledge-based Systems Characteristics of Knowledge-based Systems Conceptual Graphs Structured Knowledge Representation Conceptual Graphs & Logic Hierarchical Task Network Planning Reasoning Knowledge Representation & Reasoning Reactive Agents Concept Learning Production Systems Production System Function Cycle Knowledge Representation Agents with Internal State Constraint Programming Systems Canonical Formation Rules Inference Networks Bayesian Probability Networks & Inference Networks Expansion & Arrangement Malfunction Complexity Diagnosis & Troubleshooting Hypotheses Generation & Control Certainty Factors CF Bayesian Learning Rule Types Knowledge Representation with Rules Bayesian Probability Networks Trust Control Qualitative Simulation Examples Model Type Comparing Progression & Regression State-Space Planning Time Instant Planning Time Planning Planning Systems Case Studies Conflict Resolution Logic-based Agents Biological Neural Networks Time Interval Planning Search Space Traversal Inference Rules Plan-Space Planning Representation of Non-linear Plans Architecture of a Production System Types of Knowledge Defeasible Logic Logic Non-monotonic Logic State Representation Bidirectional Search BiS Blind Search Problem Representation Tabu Search TS Knowledge Discovery in Databases Knowledge Discovery Steps Search Algorithms in Adversary Games Minimax Algorithm Minimax Algorithm Applications Knowledge-based Systems & Human Experts Data-driven Hierarchical Classification Dempster-Shafer Theory D-S Theory Perceptron Feed-forward Neural Networks Model-based Reasoning Advantages & Disadvantages From Data to Potential Solutions Generate & Test Knowledge-based Systems & Conventional Programs Ontologies, Agents & Web Services Data, Information & Knowledge Propositional Logic Inference Mechanism A-Star Algorithm AC-3 Algorithm Artificial Neuron Model The Semantic Web Vision Reinforcement Learning Other Learning Types Parts of a Robot Robotics Advanced Interaction with the Environment Fuzzy Relations Fuzzy Logic & Fuzzy Set Theory Fuzziness Linear Planning by Regression General Diagnosis Model AB Algorithm Alpha-Beta Algorithm Fuzzy Set Properties Bayes Law K-Consistency Best-first Search BestFS Knowledge Engineering Objects Web Services Advantages & Disadvantages of Rules Environments & Abstract Architectures Abstract Agent Architectures Back Propagation Reduction Representation Hybrid Agents Ontologies BFS Breadth-first Search Delta Rule Semantic Nets Knowledge-based Systems Development Tools Architecture & Functionality Architecture of Knowledge-based Systems Structuring Elements & Basic Notions Heuristic Classification Special Knowledge Discovery Topics Frames Schemata Kohonen Networks Competition-based Neural Networks Algorithm Characteristics Typical Search Algorithms Blackboard Architecture Heuristics Classification Models Hopfield Networks NLP Natural Language Processing Natural Language Understanding Comparing Alpha-Beta with Minimax Extension Principle Hypotheses Discrimination Time Interval Logic Types of Robots Evaluation of Knowledge Representation Methods Procedural Representation Natural Language Generation Other Supervised Learning Techniques State Space Representation Fuzzy Variables, Numbers, Propositions & Rules Case-based Reasoning Advantages & Disadvantages Search Space Knowledge Discovery Problems Generate & Test Depth-first Search DFS Solution-driven Hierarchical Classification Fitness Function Principles of Genetic Algorithms Validation Verification & Validation The KQML Communication Language Diagrammatical Solution Fuzzy Reasoning Diagrammatical Plan Representation Communication & Interaction STRIPS Disadvantages Program Development for Games Basic Diagnosis Functions The KATE System Hierarchical Planning Basic Neural Network Properties ANNs Artificial Neural Networks Defuzzification Machine Vision Digital Image Description Conceptual Dependency Problem Representation Toy Problem Heuristics SA Simulated Annealing Verification Neural Network Applications The PAS System Fuzzy Set Principles Negotiation Protocols Search Spaces RDF - The Web Resources Description Model Processing Stages The Frame Problem & the STRIPS Representation Learning & Recall The FIPA ACL Communication Language Characteristics of Configuration Problems Configuration Problem Examples Characteristics of Diagnosis Problems Frame Problem Genetic Algorithms Convergence & Replacement of the Population Knowledge Engineering Tools Reproduction Knowledge Elicitation Parent Selection IDA-Star Iterative Deepening A-Star Types of Reasoning Generic Genetic Algorithm Planning Problem Definition Representation of Candidate Solutions Agents in the Semantic Web Plan Execution by Planning Agents Interaction Between Creating & Executing Plans AI Programming Languages Hypotheses Hierarchy ID Iterative Deepening Enforced Hill Climbing EHC Contracting Net Protocols Meaning Representation Producing Partial Results Malfunction Interaction Logic, Proof & Explanation Agents with Beliefs, Desires, Intentions Competition Modelling Partial Order Planner Algorithm POP Algorithm Summing up the Partial Results Expanding the Plan Graph Graph-based Planning Organisation-based Protocols Semi-grounded Operators The Travelling Salesman Problem Fuzzy Reasoning Systems Vocabulary Definition Solution Extraction Predicate Logic Equivalences & Canonical Forms Inferential Function Multi-agent Planning Gradual Tasks with Look-ahead Semantics Problem Representation Inference Mechanism B&B Branch & Bound Planning as Satisfiability Configuration Models Knowledge Elicitation Problems Agent Environments Problem Solving Applications of Genetic Algorithms Advantages & Disadvantages Knowledge Elicitation Methodologies Problem Representation Critical Points in Configuration Logical Equivalences & Canonical Forms Shannon Entropy The KADS Methodology Effectiveness & Efficiency Genetic Programming Development of Knowledge-based Systems Scenarios