Free wordnet similarity visual software for linux

WordNet::Similarity is a collection of Perl modules for computing measures of semantic relatedness.

SYNOPSIS

Basic Usage Example

use WordNet::QueryData;

use WordNet::Similarity::path;

my $wn = WordNet::QueryData->new;

my $measure = WordNet::Similarity::path->new ($wn);

my $value = $measure->getRelatedness("car#n#1", "bus#n#2");

my ($error, $errorString) = $measure->getError();

die $errorString if $error;

print "car (sense 1) bus (sense 2) = $valuen";

Using a configuration file to initialize the measure

use WordNet::Similarity::path;

my $sim = WordNet::Similarity::path->new($wn, "mypath.cfg");

my $value = $sim->getRelatedness("dog#n#1", "cat#n#1");

($error, $errorString) = $sim->getError();

die $errorString if $error;

print "dog (sense 1) cat (sense 1) = $valuen";

Printing traces

print "Trace String -> ".($sim->getTraceString())."n";

Introduction

We observe that humans find it extremely easy to say if two words are related and if one word is more related to a given word than another. For example, if we come across two words, car and bicycle, we know they are related as both are means of transport. Also, we easily observe that bicycle is more related to car than fork is. But is there some way to assign a quantitative value to this relatedness? Some ideas have been put forth by researchers to quantify the concept of relatedness of words, with encouraging results.

Eight of these different measures of relatedness have been implemented in this software package. A simple edge counting measure and a random measure have also been provided. These measures rely heavily on the vast store of knowledge available in the online electronic dictionary -- WordNet. So, we use a Perl interface for WordNet called WordNet::QueryData to make it easier for us to access WordNet. The modules in this package REQUIRE that the WordNet::QueryData module be installed on the system before these modules are installed.

WordNet::Similarity::PathFinder is a Perl module to implement path finding methods (by node counting) for WordNet::Similarity measures of semantic relatedness.

SYNOPSIS

use WordNet::QueryData;

my $wn = WordNet::QueryData->new;

use WordNet::Similarity::PathFinder;

my $obj = WordNet::Similarity::PathFinder->new ($wn);

my $result = $obj->parseWps($wps1, $wps2);

my @paths = $obj->getShortestPath("dog#n#1", "cat#n#1", "n", "wps");

my ($length, $path) = @{shift @paths};

defined $path or die "No path between synsets";

my @paths = $obj->getAllPaths("worship#v#1", "adore#v#1", "v", "wps");

my ($length, $path) = @{shift @paths};

defined $path or die "No path between synsets";

my @paths = $obj->getShortestPath("02895418", "02724985", "n", "offset");

my ($length, $path) = @{shift @paths};

defined $path or die "No path between synsets";

Introduction

This class is derived from (i.e., is a sub-class of) WordNet::Similarity.

The methods in this module are useful for finding paths between concepts in WordNets is-a taxonomies. Concept A is-a concept B if, and only if, B is a hypernym of A or A is in the hypernym tree of B. N.B., only nouns and verbs have hypernyms.

The methods that find path lengths (such as getShortestPath() and getAllPaths() compute the lengths using node-counting not edge-counting. In general, the length of a path using node-counting will always be one more than the length using edge-counting. For example, if concept A is a hyponym of concept B, then the path length between A and B using node-counting is 2, but the length using edge-counting is 1. Likewise, the path between A and A is 1 using node-counting and 0 using edge-counting.

WordNet::SenseRelate::AllWords is a Perl module to perform Word Sense Disambiguation.

SYNOPSIS

use WordNet::SenseRelate::AllWords;
use WordNet::QueryData;
my $qd = WordNet::QueryData->new;
my $wsd = WordNet::SenseRelate::AllWords->new (wordnet => $qd,
measure => WordNet::Similarity::lesk);
my @results = $wsd->disambiguate ();

WordNet::SenseRelate::AllWords implements an algorithm for Word Sense Disambiguation that uses measures of semantic relatedness. The algorithm is an extension of an algorithm described by Pedersen, Banerjee, and Patwardhan[1]. This implementation is similar to the original SenseRelate package but disambiguates every word in the given context rather than just single word.

Vector Visuals provides an easy-to-use, object-based API for creating and manipulating Java2D-rendered shapes and images. The project features object embedding, dynamic connectors, and multithreaded task support.
Vector Visuals has been downloaded thousands of times, and is already in use in a variety of applications. A few that we know of are a business ontology viewer, a particle simulator, and a childrens e-book builder.
Enhancements:
- This release of is a combination of bugfixes and new features.
- Persistence delegates were added for all of the Vector Visuals objects.
- The examples were moved out into their own source folder.
- Additionally, the entire product is now licensed under a permissive "New BSD"-style license.

The Databionic MusicMiner is a browser for music based on data mining techniques. You can create MusicMaps to visualize the similarity of songs and artists. Explore your music and create playlists based on the paradigm of geographical maps!
Main features:
- Automatic parsing of a folder tree with music files (MP3, OGG, WMA, M4A, MP2, WAV).
- Automatic description of digital audio files by sound.
- Creation of MusicMaps to navigate the sound space based on the paradigm of geographical maps.
- Visual creation of playlists.
- Similarity search in music collection based on sound.
- Customizable hierarchichal browsing of the database by e.g. genre/artist/album or year/artist.
- Flexible database including the seperate storage of several artists per song, albums and playlists as part of a playlist.
- Import and export of meta information based on XML.
Enhancements:
- This release offers better sound descriptors resulting from more experiments with audio features.
- The processing of audio files is more robust, the import of meta information is much faster, and more audio formats are supported.
- MusicMiner now also runs on OS X with some manual setup work described in a howto.

Business Process Visual ARCHITECT is a full-featured business process modeler that supports the latest OMG Business Process Modeling Notation (BPMN).
Business Process Visual ARCHITECT provides an easy-to-use diagramming environment for you to model your business process, and is a proven solution for bridging the gap between business analysts and IT professionals.
Main features:
- Frictionless business modeling environment
- On-the-fly syntax check and correction according to BPMN specification
- Advanced printing facility for outputting large business process diagram
- Share business process diagram among your company with Teamwork Server
- Incorporate user-defined images to the business process diagram to increase the readability.
Enhancements:
- Branch and tag capabilities were added to the VP Teamwork Server, including Subversion and CVS repository integration.
- This allows different modeling projects to be run in parallel while keeping the release quality project stable in the trunk.
- There were also a number of enhancements for various other features.