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Description

232

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Description

232

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Description

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Description

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Description

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Description

Uses Weka webservices to build a J48 tree from the Iris dataset and displays it as a string.

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Description

Uses Weka webservices to build a J48 tree from the Iris dataset and displays it as a string.

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Description

Uses Weka webservices to build a J48 tree from the Iris dataset and displays it as a string. A 10-fold cross validation is also performed on the tree.

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Description

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Description

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Description

Concatenates the strings !!

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Description

A simple example of concatenating strings.

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Description

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Description

An example of a decision support workflow using the Weighted Sum Model.

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Description

An example of a decision support workflow using the Weighted Sum Model.

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Description

Extracts terminology and definitions from a domain text in Slovene and English. It implements 3 methods for definition extraction: lexico-syntactic patterns, WordNet based extraction and knowledge-rich context (terminology-based) extraction that can be used individually or combined. (More details in: Pollak et al. 2012, Informatica)

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Description

This workflow implements the term and definition extraction on a corpus of documents with three methods developed by Senja Pollak et al. The results of the methods are merged and displayed. Note that a new version will be available in the following days!

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Description

The definition extraction workflow extracts domain terminology and a set of definition candidates from domain corpus in Slovene or English. The definition extraction is based on three different methods: the pattern-based, the term-based and the wordnet-based definition extraction method.

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Description

The definition extraction workflow extracts domain terminology and a set of definition candidates from domain corpus in Slovene or English. The definition extraction is based on three different methods: the pattern-based, the term-based and the wordnet-based definition extraction method.

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Description

You can also delay the execution of your workflows. This example shows how.

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Description

This is an example of the first two use cases for the medical informatics platform of the Human Brain project.

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Description

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Description

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Description

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Description

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Description

Interactive microarray experiment on GSE2990 breast cancer dataset done in two consecutive subgroup discovery steps: first a standard subgroup discovery is applied to find interesting groups of patients in terms of gene expression and then the subgroup chosen by the user is explained via GO/KEGG concepts. Lastly, genes covered by the explanations are used to query Biomine.

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Description

Motivational example of explaining subgroups. A synthetic dataset of bank customers is used to demonstrate the use of the methodology.

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Description

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Description

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Description

Loops through a list of integers and multiplies each one by two.

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Description

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Description

Example of using Hedwig on a small example.

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Description

Extracted ICCC terminology

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Description

Example of using Aleph on the Michalski trains dataset.

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Description

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Description

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Description

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Description

Example of propositionalizing the Michalski trains dataset using RSD and then building a decision tree using Weka services.

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Description

Attempting to use RSD for feature construction.

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Description

Example of propositionalizing the Michalski trains dataset using RSD by reading it from a MySQL database and then building a decision tree using Weka services.

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Description

Example of propositionalizing the Michalski trains dataset using RSD by reading it from a MySQL database and then building a decision tree using Weka services.

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Description

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Description

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Description

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Description

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Description

running aleph with GO

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Description

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Description

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Description

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Description

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Description

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Description

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Description

Data flow diagram

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Description

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Description

A simple example of concatenating strings.

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Description

The second MUSE workflow prototype for the children story use case (Tuk the hunter)

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Description

A precomputed version of the second MUSE workflow prototype for the children story use case (Tuk the hunter). This version is for testing purposes only because the mapping to knowledge representation widget always returns the same mapping (the real version is very slow because the problem domain of the search is huge).

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Description

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Description

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Description

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Description

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Description

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Description

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Description

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Description

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Description

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Description

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Description

This workflow loads the Iris dataset from the UCI repo, transforms it into an Orange Data Table, builds a classifier using Classification Tree and applies the classifier in the test dataset. Moreover, it performs cross validation (10-fold) and calculates the classification accuracy.

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Description

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Description

Extraction of definitions from Slovene and English based on lexico-syntactic patterns (more details in Pollak et al., Informatica 2012).

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Description

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Description

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Description

Loads training and test dataset differently. Trains Naive Bayes Classifier using training set and tried to apply classifier to test dataset. However, APPLY CLASSIFIER DOES NOT WORK.

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Description

dime el formato

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Description

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Description

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Description

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Description

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Description

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Description

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Description

Example of propositionalizing the Michalski trains dataset using RSD by reading it from a MySQL database and then building a decision tree using Weka services.

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Description

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Description

Example of using SDM-Aleph on a toy example with three ontologies.

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Description

Example of using SDM-SEGS on a toy example with three ontologies.

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Description

This workflow utilizes the new semantic data mining system SDM-SEGS which can be used to discover subgroup descriptions from ranked data as well as from class labeled data with the use of input OWL ontologies.

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Description

Example of using SDM-SEGS on Prem's toy data.

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Description

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Description

A basic SegMine workflow which loads the hMSC data (from http://segmine.ijs.si/use_cases/), ranks the genes, cut off those with logFC close to 0, runs SEGS and puts the some genes into Biomine search to show an example network.

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Description

Example of arithmetics

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Description

Uses WEKA webservices to cross validate Naive Bayes on the Iris dataset.

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Description

Based on the jobs dataset.

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Description

This workflow takes the Iris dataset and builds a Naive bayes classifier from it.

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Description

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Description

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Description

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Description

Extracting terminology from a text corpus in English or Slovene. (More information in Pollak et al., 2012 (Konvens)). Integrates and develops over term extracion by Vintar (2010) and linguistic annotation by T. Erjavec (2011).

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Description

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Description

An example workflow using the ToTrTaLe web-service for tokenization, morphosyntactic annotation and lemmatization.

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Description

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Description

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Description

Experiment Description

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Description

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Description

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Description

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Description

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Description

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Description

Word cloud of virtual reality articles

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Description

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Description

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Description

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Description

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Description

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Description

Classification applying WEKA's Random Forest to the IRIS UCI dataset

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Description

We try to get a wordified dataset of chemicals originated as a relational structure.

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Description

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Description

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