Crowdmap Crowdmap allows you to set up your own deployment of the Ushahidi Platform without having to install it on your own web server. Crowdmap is the fastest, simplest installation of the Ushahidi platform. Within minutes you'll be up and running with your own installation, mapping reports events and visualizing information. Things You Can Do With Crowdmap Monitor Elections Use the power of the crowd to monitor and visualize what went right, and what went wrong, in an election. Map Crisis Information Whether it's a natural disaster, epidemic or political crisis, Crowdmap is built to handle information coming out of a crisis. Curate Local Resources Crowdsourcing isn't just for emergencies, you can use it for local knowledge and business too. Document A Zombie Invasion How else will you survive the coming apocalypse? Learn more on the Crowdmap Website .
Recommender Systeme – Winfwiki Aus Winfwiki 1 Einleitung Recommender Systeme sind spezielle Empfehlungssysteme, die insbesondere im Online-Handel genutzt werden. Sie erzeugen Produktvorschläge für interessierte Kunden, die auf die Vorlieben und Wünsche des Nutzers zugeschnitten zu sein scheinen. 1.1 Problemstellung / Ausblick Auf Basis der genannten Definition werden im Rahmen dieser Arbeit sowohl die reine Technologie als auch mögliche Einsatzgebiete von Recommender Systemen einer eingehenden Betrachtung unterzogen, um eine klare Einschätzung zu tatsächlichen und potenziellen Nutzwerten von Empfehlungssystemen abgeben zu können. 1.2 Einordnung Mit der exponentiell steigenden Nutzung des Internets seit Anfang der neunziger Jahre und der daraus resultierenden Menge an Informationsangeboten steigt das Bedürfnis sowohl auf Nutzer- als auch auf Anbieterseite, Informationen zielgerecht auf die jeweiligen Interessen- und Anwendungsgebiete maßzuschneidern. 1.3 Historie 2 Klassifizierung von Recommender Systemen (vgl.
Clique Clique is a modification of the Elgg social networking platform. Clique provides users with a social network platform that enables them to keep control over their privacy. This includes, for example, fine grained access control and configuration of multiple faces (e.g. family, personal, professional) that can be used for interactions with other users. When posting a data item, e.g., name, birthday or profile photo on the site, the user can define for every single other user whether they should be able to see it or not. collections – contacts are organised in collections, roughly corresponding with social circles. The Clique demonstrator can be accessed through The library comprises only the implementations of the credential system features, additional required components for building various kinds of practical systems are not included in the library. Who may be interested in the component? We plan to extend Clique with more features. Version: Size: 2,09 MB Author:
Romulus - Domain Driven Design and Mashup Oriented Development The main concept of is researching on novel methods for increasing productivity and reliability of web software development, in particularly, focused on Java web development. proposal is based on recognising some of the deficiencies of standard Java Enterprise Edition, and proposing a new paradigm for developing web applications taking advantage of new trends in software engineering, such as domain driven design combined with agile development methodologies, and some of the principles from Ruby on Rails. In order to have a serious impact, the project does not start from scratch, it is based on two mature open source projects, Roma and LIFERAY, which will be extended according to this proposal needs and following an open source project development methodology, in order to disseminate and exploit the results of the project. Integrating a “Mash-up oriented development” in the process. Web Services Mashups, such as Google Maps or Yahoo Pipes. Develop vertical solutions
Guiding Principles for the Open Semantic Enterprise From MIKE2.0 Methodology Introduction An open semantic enterprise is an organization that uses 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. It does so using some or all of the seven guiding principles noted herein.[1] These guiding principles do not necessarily mean open data nor open source. These practices do not require replacing current systems and assets; they can be applied equally to public or proprietary information; and they can be tested and deployed incrementally at low risk and cost. Like any change in practice or learning, embracing the open semantic enterprise is fundamentally a people process. Summary: The Seven Principles and Their Benefits The natural scope of the open semantic enterprise is in knowledge management and representation. The RDF Data Model
Engine/DataModel/Entities/ElggGroup We're rewriting our docs. This page has been ported to github. Join the effort! The ElggGroup entity type represents groups within an Elgg install. Introduction Beyond the standard ElggEntity properties, ElggGroups also support: name The group's plain text name description A description of the group A group in Elgg is an entity that users can join, leave and post content to. A group acts as a container for other entities, and has other methods provided by ElggGroup in order to manage content and membership. ElggGroup and default groups It's important to draw a distinction between the default groups supplied in the Elgg distribution - which have profile pages, forums and draw content to the front - and ElggGroup, the ElggEntity specialization class that provides group-level functionality. The default group as provided by Elgg is an example. Writing a group-aware plugin Plugin owners need not worry too much about writing group-aware functionality, but there are a few key points: Uploading content
Collaborative Protege The format of the Changes and Annotation ontology (ChAO) has changed in Protege 3.4.2 release. If have an existing ChAO project created with an earlier version of Protege and would like to take advantage of the new features, please follow the upgrade instructions from here. Collaborative Protege is an extension of the existing Protege system that supports collaborative ontology editing. In addition to the common ontology editing operations, it enables annotation of both ontology components and ontology changes. It supports the searching and filtering of user annotations, also known as notes, based on different criteria. The multi-user mode - allows multiple clients to edit simultaneously the same ontology hosted on a Protege server. This user guide applies to both multi-user and standalone mode of Collaborative Protege. Collaborative Protege is distributed with the full installation of Protege. Stand-alone mode Install the full distribution of the latest version of Protege 3.*.
Semantische Modellierung Als integraler Teil des Ontology Engineerings führen wir unternehmensinterne Projekte zum semantischen Terminologiemanagement durch. Als immaterielles Ergebnis erhält ein Unternehmen ein gemeinsames und besseres Verständnis zu seinen zentralen Begriffen. Vor allem die Kommunikation zwischen Kunde, Entwicklung, Vertrieb und Management verbessert sich erheblich. Als "anfassbares" Ergebnis liefern wir an den Kunden eine ausführliche Dokumentation seiner Terminologie in verschiedenen Formaten und Visualisierungen aus. Wir erstellen semiformale und formale Terminologien - sogenannte Ontologien - sowohl auf Schema wie auf Instanz-Ebene. Leichtgewichtige Ontologien modellieren wir bevorzugt in SKOS. Heavy weight (stark axiomatisierte oder regelbasierte) Ontologien modellieren wir je nach Anforderung in RDFS, OWL oder F-Logic respektive Object Logic. Unsere Spezialität ist das "Tuning" von OWL-Ontologien, um sie für large scale inferencing mit regelbasierten Rule-Engines fit zu machen.
Virtuoso Open-Source Wiki : Virtuoso Sponger Sponger ontology mappers peform the the task of generating RDF instance data from extracted metadata (non-RDF) using ontologies associated with a given data source type. They are typically XSLT (using GRDDL or an in-built Virtuoso mapping scheme) or Virtuoso PL based. Virtuoso comes preconfigured with a large range of ontology mappers contained in one or more Sponger cartridges. Figure 9: Sponger architecture Below is an extract from the stylesheet /DAV/VAD/rdf_cartridges/xslt/flickr2rdf.xsl, used for extracting metadata from Flickr images. <xsl:template match="owner"> <rdf:Description rdf:nodeID="person"> <rdf:type rdf:resource=" /> <xsl:if test="@realname ! Cartridge Registry The SYS_RDF_MAPPERS table definition is as follows: Cartridge Invocation The Virtuoso SPARQL processor supports IRI dereferencing via the Sponger. Sponger cartridges are invoked during the aforementioned pipeline as follows: Figure 10: Sponger cartridge invocation flowchart
Home