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Semantic Tools
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METEOR-S: Process Designer
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The METEOR-S Process Design tool provides BPEL process Developer with an easy to use GUI for generating BPEL Web processes. The tool also generates a process WSDL that can be used to deploy the business process on a BPEL engine.
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METEOR-S: Radiant (WSDL-S/SAWSDL Annotation Tool)
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The Radiant Eclipse plugin provides a UI for annotating existing WSDL documents into WSDL-S/SAWSDL via an OWL Ontology and publishing them.
Radiant comes with an ontology viewer based on the UMBC's Cobra Ontology Viewer.
Note: We recommend jUDDI for use with the publishing feature, though other UDDI Registries may work.
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Lumina: WSDL-S/SAWSDL Web Service Discovery
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Lumina is an Eclipse plugin that provides a GUI for the Discovery of Semantic Web Services (WSDL-S/SAWSDL).
Lumina uses the Ontology Viewer in Radiant to facilitate Semantic Web Service Discovery.
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Semantic Visualization: OntoVista
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In OntoVista, Semantic visualization aims at providing a meaningful display of ontology-concepts. What is considered meaningful heavily depends on the domain the ontology is applied on or the view of the ontology that the user chose for a particular visualization. For example, a tree layout of a graph typically reflects an inheritance hierarchy or a partonomy/meronymie relationship between the nodes in the tree. Furthermore, node/edge shapes and colors can code for different types of objects/relationships. With OntoVista, we provide an ontology visualization tool that allows the customization of layouts using an additional ontology, called the visualization ontology. This ontology gives us a tight control over the way that potentially each single concept and relationship in the data-ontology is displayed in our layouts. Additionally it provides advanced searching and semantic filtering capabilities.
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Semantic Visualization: SAV (Semantic Analytics Visualization)
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"Semantic Analytics Visualization" (SAV) is a 3D visualization tool for Semantic Analytics. It has the
capability for visualizing ontologies and meta-data including annotated web documents,
images, and digital media such as audio and video clips in a synthetic
three-dimensional semi-immersive environment. More importantly, SAV
supports visual semantic analytics, whereby an analyst can interactively investigate
complex relationships between heterogeneous information. Use of a
Virtual Reality technology allows it to provide a highly interactive
interface. The backend of SAV supports query processing and semantic
association discovery developed in the SemDis project. Using a virtual
laser pointer, the user can select nodes in the scene and either play digital media,
display images, or load annotated web documents. SAV can also display
the ranking of web documents as well as the ranking of paths (sequences of
links). SAV supports dynamic specification of sub-queries of a given graph
and displays the results based on ranking information, which enables the users
to find, analyze and comprehend the information presented quickly and accurately.
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Semantic Visualization: SET (Semantic EventTracker)
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Semantic EventTracker (SET) is a highly interactive visualization tool for
tracking and associating activities (events) of suspects in a Spatially
Enriched Virtual Environment (SEVE). "Event" information is extracted
from ontologies that enable a user to discover semantic associations
between events using thematic and topological relations, including RDF
graphs that represent metadata and ontological teams. For example, a
suspect A traveled to city M in two days where he met another suspect B.
After 3 days, they took a flight where another suspect C was also
traveling and they all arrived at a nuclear facility in city N.
Additionally, a week prior, suspect C had visited a bomb manufacturing
facility in country X. A 2D map is generated using geospatial information
which is applied as a texture on 3D objects. Temporal data is represented
as a 3D multi-line in space that connects sequencing events. The slope of
the line depends upon the time-distance between two events; time-distance
indicates how far apart two events occurred in time, for example, this
could depict how fast a suspect traveled from one place to another. A
steep slop indicates greater time-distance. The line is semantically
marked with 3D objects that can be selected by the user's virtual hand.
Upon a marking's selection, SET displays in SEVE metadata information such
as digital images, 3D models, web documents and it can also play audio and
video clips. Multiple multi-lines can be visualized on the same map to
help discover path crossings, meeting times, contacts of suspects, etc, of
tracked suspects.
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SemDis: SemdisAPI (Semantic Discovery Application Program Interface)
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The SemDis API is a simple yet flexible set of Interfaces that
are intended to be a basis for various implementations for RDF
data access. The SemDis API is suitable for the type of
algorithms and applications developed in the SemDis project.
Java implementations of the API are available for (i)
Main-memory-based access; (ii) Semagix-Freedom access; (iii)
Java-bindings for BRAHMS (the underlying UML model is the same
as that of BRAHMS. Therefore, porting code from Java to C++
for use in Brahms is a straightforward task. Additionally,
Oracle's RDF-based access through this API is under development.
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Demos Available
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SemDis: BRAHMS (Main-memory storage for RDF/S)
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This tool is a main-memory, read-only fast access database for RDF data. The tool, implemented in C++, reads one or more RDF files from disk and creates a snapshot that can later be read quickly in subsequent usage of the database.
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SemDis: TOntoGen (Test Ontology Generation Tool)
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This tool allows for synthetic generation of instances of an ontology. A human specifies statistics that are expected on each class, relationships in the ontology. This tool is available as a Protege plug-in.
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Glycomics: GlycOViz (GlycO Visualizing Tool) *No longer in general use
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GlycOViz is an ontology browser for viewing and browsing the Glycomics Ontology.
The default view of the ontology is a tree view similar to that of Protege. Also available is a TouchGraph representation of the ontology. The different frames and tabs are logically interconnected. When the user clicks on a class in the tree view, it comes into focus in the graph view and vice versa. At the same time, the properties tab and the instances tab are updated with the properties and instances of the selected class. The natural language description and provenance information of a particular class or instance is always displayed in a text area at the bottom of the applet. Overall, the tool combines the advantages of both a hierarchical structure, provided by the tree view, and a graph structure, provided by the TouchGraph interface.
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