Free querydata 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.