Wandora is a useful and open source instrument that can be used to create and manage topic maps.
Wandora comes with a complex GUI, a layered presentation of knowledge, several data storage / extraction options, import and export capabilities, and open plug-in architecture.
Furthermore, Wandora also includes an embedded HTTP server. Wandora suits well for rapid ontology construction and knowledge mashups.
Here are some key features of "Wandora":
Layered representation of knowledge:
· Construct the knowledge using several different layers, each containing only part of the knowledge
· You can view only parts of data and hide parts that don't intrest you
· Automatic merging of different and even distributed data sources
· Protect some parts of topic map by allowing only read access
· Use nested layer stacks to create tree like layer structures
Several data storage options:
· Memory based topic map for very fast processing of relatively small topic maps
· Relational database storage makes it possible to use topic maps of virtually unlimited size
· Relational database storage enables easy usage of remote third party topic maps
Import data in several formats:
· Imports topic maps in XTM and LTM formats
· Imports RDF data in RDF(S) and N3 formats
· Imports OBO flat file format ontologies such as The Gene Ontology
· Imports any valid XML document with proper XSL converting the XML to XTM
· Converts and imports any SQL database to a topic map
· Clipboard import and export of topics and associations
Extract topic maps from various different sources:
· Firefox plugin to extract directly from Firefox WWW browser.
· Note some extractors described below doesn't work with Firefox plugin.
· Convert MP3 ID3 metadata to a topic map
· Convert JPEG metadata to a topic map
· Convert PDF metadata to a topic map
· Convert emails and email repositories to a topic map
· Convert file system structures to a topic map
· Convert HTML site structure to a topic map
· Convert IMDB datafiles to a topic map
· Convert freedb database entries to a topic map
· Wikipedia extractor and more general MediaWiki extractor
· Convert Last.fm XML feeds to a topic map
· Convert BibTeX files to a topic map
· Convert RIS files to a topic map
· Convert simple HTML tables to topic map associations
· HTML property table extractor
· HTML association table extractor
· Convert simple HTML lists to topic maps
· HTML superclass-subclass list extractor
· HTML instance list extractor
· Convert simple text documents to topic map occurrences
· Convert RSS 2.0 feeds to topic maps
· Microformat extractors
· Convert geo microformat snippets to topic maps
· Convert adr microformat snippets to topic maps
· Convert hcalendar microformat snippets to topic maps
· Convert hcard microformat snippets to topic maps
· Extract topic maps from Flickr
· Extract topic maps from YouTube
· Extract topic maps from Digg
· Extract topic maps from Del.icio.us
· OpenCalais classifier
· OpenCyc extractor
· Analyze topic maps
· Compare topic maps.
· Clustering coefficient for any topic set including average clustering coefficient of a topic map.
· Topic map diameter and average path length.
· Topic map connection count and distribution (Topic map degree distribution).
· SOM classifier to analyze topics with associations.
· Export the topic map as a collection of static HTML pages
· Embedded HTTP server allows you to publish topic maps as a dynamic HTML site.
· Wandora Piccolo combination allows you to set up a large scale dynamic WWW site based on the topic maps
· Create and export Lucene search index from any topic map
· Download and manage subject locator files
· Build your own topic map application using Wandora's SOAP Web Service
· Export topic maps as Graph Modeling Language, GraphML, and GraphXML graphs.
Generate simple topic map graphs:
· Random graph generator
· Fully connected graph generator
· Tree graph generator
· Linear list graph generator
· Finite group graph generator
· Platonic solid graph generator
Plugin architecture makes it easy to add custom functionality:
· Tools that process topic map, for example cleaning or reconstructing data
· Importing data in different formats, for example from existing relational database
· Exporting data in different formats
· Reading and automatic processing of metadata from a collection of files
· Customized ways to represent the data in the topic map
· Custom publishing methods
· 1.0 GHz Intel Pentium 3 or AMD
· Java JRE 6 or newer
· Memory: 256 MB RAM
· Disk space: 100 MB
What's New in This Release: [ read full changelog ]
· Undo and redo all topic map operations. Finally.
· Updated Twitter, Facebook and Europeana extractors.
· Numerous fixes and enhancements.