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Web Ontology Language

The OWL family contains many species, serializations, syntaxes and specifications with similar names. OWL and OWL2 are used to refer to the 2004 and 2009 specifications, respectively. Full species names will be used, including specification version (for example, OWL2 EL). When referring more generally, OWL Family will be used. History[edit] Early ontology languages[edit] Ontology languages for the web[edit] In 2000 in the United States, DARPA started development of DAML led by James Hendler.[12] In March 2001, the Joint EU/US Committee on Agent Markup Languages decided that DAML should be merged with OIL.[12] The EU/US ad hoc Joint Working Group on Agent Markup Languages was convened to develop DAML+OIL as a web ontology language. OWL started as a research-based[14] revision of DAML+OIL aimed at the semantic web. Semantic web standards[edit] The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. Related:  Explore Later

Analytic–synthetic distinction The analytic–synthetic distinction (also called the analytic–synthetic dichotomy) is a conceptual distinction, used primarily in philosophy to distinguish propositions (in particular, statements that are affirmative subject-predicate judgments) into two types: analytic propositions and synthetic propositions. Analytic propositions are true by virtue of their meaning, while synthetic propositions are true by how their meaning relates to the world.[1] However, philosophers have used the terms in very different ways. Furthermore, philosophers have debated whether there is a legitimate distinction. Kant[edit] Conceptual containment[edit] analytic proposition: a proposition whose predicate concept is contained in its subject conceptsynthetic proposition: a proposition whose predicate concept is not contained in its subject concept Examples of analytic propositions, on Kant's definition, include: "All bachelors are unmarried."" Kant's own example is: "All bachelors are unhappy."" [edit]

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.

Jena Framework Security information and event management Security Information and Event Management (SIEM) is a term for software and products services combining security information management (SIM) and security event manager (SEM). SIEM technology provides real-time analysis of security alerts generated by network hardware and applications. SIEM is sold as software, appliances or managed services, and are also used to log security data and generate reports for compliance purposes.[1] The acronyms SEM, SIM and SIEM have been sometimes used interchangeably.[when?] As of February 2014, Mosaic Security Research identified 64 SIEM and log management products.[4] Capabilities[edit] Data Aggregation: Log management aggregates data from many sources, including network, security, servers, databases, applications, providing the ability to consolidate monitored data to help avoid missing crucial events.Correlation: looks for common attributes, and links events together into meaningful bundles. See also[edit] References[edit]

Apache Jena - Apache Jena SSSW OWL Examples Abstract This document presents some simple example OWL ontologies and discusses some of the inferences that can be made about the classes and individuals in those ontologies. Status of this document This version produced 12th July, 2005. Introduction The OWL Web Ontology Language describes a language for ontologies. The purpose of this hands on session is to explore some of the effects of applying reasoning in OWL. For each of these example inferences, you should look at the underlying model and try and work out why the inference is being made. For all of the examples, feel free to experiment and change the definitions given in order to better understand what the operators mean and how they interact. There are no solutions given for this session, although there are explanations provided for many of the inferences. Examples People. References OWL Web Ontology Language Semantics and Abstract Syntax.

REX | Jerry’s Brain app: background and support page Thank you for buying and installing the Jerry’s Brain app (if you haven’t yet but would like to, click here). This page should explain what you’ve gotten into and where to turn with questions or comments. I did not create TheBrain app, nor am I in charge of the servers or the App Store side. First, some important things you should know: Jerry’s Brain is an experiment, one person’s visual journal, not a professionally published database. You can learn a bunch more by watching this short intro to my Brain. My Brain is always accessible freely through your desktop browser at JerrysBrain.com, but that version doesn’t work well on other devices. What is this software? Jerry’s Brain is built on PersonalBrain, PC/Mac/Linux software created by TheBrain, a company based in Los Angeles. Who are you? In December 1997, when I first saw PersonalBrain, I was a technology industry analyst, and it was my job to interview startups like this one. Why are you doing this?

OWL Event correlation Event correlation is a technique for making sense of a large number of events and pinpointing the few events that are really important in that mass of information. History[edit] Event correlation has been used in telecommunications and industrial process control since the 1970s, in network management and systems management since the 1980s, in IT service management and event-based systems since the 1990s, and in business activity monitoring (BAM) since the early 2000s. Event correlation in integrated management[edit] The goal of integrated management is to integrate the management of networks (data, telephone and multimedia), systems (hosts and applications) and IT services in a coherent manner. Events and event correlator[edit] Event correlation usually takes place inside one or several management platforms (also known as Network Management Stations or Network Management Systems). Step-by-step decomposition[edit] Event filtering[edit] Event aggregation[edit] Event masking[edit] See also[edit]

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