Provenance Provenance (from the French provenir, "to come from"), is the chronology of the ownership, custody or location of a historical object.[1] The term was originally mostly used in relation to works of art, but is now used in similar senses in a wide range of fields, including archaeology, paleontology, archives, manuscripts, printed books, and science and computing. The primary purpose of tracing the provenance of an object or entity is normally to provide contextual and circumstantial evidence for its original production or discovery, by establishing, as far as practicable, its later history, especially the sequences of its formal ownership, custody, and places of storage. The practice has a particular value in helping authenticate objects. Comparative techniques, expert opinions, and the results of scientific tests may also be used to these ends, but establishing provenance is essentially a matter of documentation. Works of art and antiques[edit] Wines[edit] Archives[edit] Books[edit]
Faceted navigation for document discovery Better search with faceted navigation Text search is one of the most important ways that users of enterprise content can find the documents they need. Unfortunately, there are a number of reasons why enterprise text search systems often work less well than search of the public Internet (Enterprise Search: Tough Stuff, Rajat Mukherjee and Jianchang Mao. ACM Queue vol. 2, no. 2, April 2004). Thus there is an opportunity to improve search within enterprises by using metadata. While there are several different ways for a user to specify metadata conditions, this article is about one that has special advantages: faceted navigation. Back to top Example of faceted navigation The faceted navigation search interface of Croton is shown in Figure 1. This example illustrates three key features of faceted navigation: The user selects metadata conditions by clicking on values presented by the application.Only metadata values that lead to documents are presented. Figure 1. The WITS document set Table 1.
RDF Schema 1.1 Abstract RDF Schema provides a data-modelling vocabulary for RDF data. RDF Schema is an extension of the basic RDF vocabulary. Status of This Document This section describes the status of this document at the time of its publication. This document is an edited version of the 2004 RDF Schema Recommendation. This document was published by the RDF Working Group as a Recommendation. This document has been reviewed by W3C Members, by software developers, and by other W3C groups and interested parties, and is endorsed by the Director as a W3C Recommendation. This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. Table of Contents 1. RDF Schema provides a data-modelling vocabulary for RDF data. This document is intended to provide a clear specification of RDF Schema to those who find the formal semantics specification [RDF11-MT] daunting. RDF Schema is a semantic extension of RDF. 2. 2.1 rdfs:Resource 2.2 rdfs:Class 3. Note
Tools OWL Web Ontology Language Guide W3C Recommendation 10 February 2004 New Version Available: OWL 2 (Document Status Update, 12 November 2009) The OWL Working Group has produced a W3C Recommendation for a new version of OWL which adds features to this 2004 version, while remaining compatible. This version: Latest version: Previous version: Editors: Michael K. Chris Welty, IBM Research, Deborah L. Please refer to the errata for this document, which may include some normative corrections. See also translations. Copyright © 2004 W3C® (MIT, ERCIM , Keio), All Rights Reserved. Abstract The World Wide Web as it is currently constituted resembles a poorly mapped geography. The OWL Web Ontology Language is intended to provide a language that can be used to describe the classes and relations between them that are inherent in Web documents and applications. This document demonstrates the use of the OWL language to
RDF 1.1 Semantics Abstract This document describes a precise semantics for the Resource Description Framework 1.1 [RDF11-CONCEPTS] and RDF Schema [RDF11-SCHEMA]. It defines a number of distinct entailment regimes and corresponding patterns of entailment. Status of This Document This section describes the status of this document at the time of its publication. This document is part of RDF 1.1 document suite. This document was published by the RDF Working Group as a Recommendation. Please see the Working Group's implementation report. This document has been reviewed by W3C Members, by software developers, and by other W3C groups and interested parties, and is endorsed by the Director as a W3C Recommendation. This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. Table of Contents Notes Notes in this style indicate changes from the 2004 RDF 1.0 semantics. Notes in this style are technical asides on obscure or recondite matters. 1. 2. 3. semantic extension . entailment regime .
Curated knowledge RDF Primer The Resource Description Framework (RDF) is a language for representing information about resources in the World Wide Web. This Primer is designed to provide the reader with the basic knowledge required to effectively use RDF. It introduces the basic concepts of RDF and describes its XML syntax. It describes how to define RDF vocabularies using the RDF Vocabulary Description Language, and gives an overview of some deployed RDF applications. It also describes the content and purpose of other RDF specification documents. 1. The Resource Description Framework (RDF) is a language for representing information about resources in the World Wide Web. RDF is intended for situations in which this information needs to be processed by applications, rather than being only displayed to people. RDF is based on the idea of identifying things using Web identifiers (called Uniform Resource Identifiers, or URIs), and describing resources in terms of simple properties and property values. <? 2. (URL).
RDF 1.1 Concepts and Abstract Syntax Abstract The Resource Description Framework (RDF) is a framework for representing information in the Web. This document defines an abstract syntax (a data model) which serves to link all RDF-based languages and specifications. The abstract syntax has two key data structures: RDF graphs are sets of subject-predicate-object triples, where the elements may be IRIs, blank nodes, or datatyped literals. They are used to express descriptions of resources. Status of This Document This section describes the status of this document at the time of its publication. This document is part of the RDF 1.1 document suite. This document was published by the RDF Working Group as a Recommendation. This document has been reviewed by W3C Members, by software developers, and by other W3C groups and interested parties, and is endorsed by the Director as a W3C Recommendation. This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. Table of Contents 1. Any IRI or literal denotes
Information Systems SPARQL SPARQL (pronounced "sparkle", a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language, that is, a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework format.[2][3] It was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium, and is recognized as one of the key technologies of the semantic web. On 15 January 2008, SPARQL 1.0 became an official W3C Recommendation,[4][5] and SPARQL 1.1 in March, 2013.[6] SPARQL allows for a query to consist of triple patterns, conjunctions, disjunctions, and optional patterns.[7] Implementations for multiple programming languages exist.[8] "SPARQL will make a huge difference" making the web machine-readable according to Sir Tim Berners-Lee in a May 2006 interview.[9] Advantages[edit] The example below demonstrates a simple query that leverages the ontology definition "foaf", often called the "friend-of-a-friend" ontology.
"Core types xsd:string - Character strings (but not... Artificial intelligence AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based