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232

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232

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232

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Workflow for analysis of financial reports

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Example of arithmetics

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

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

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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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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

Workflow for classification of financial reports.

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Description

Concatenates the strings !!

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A simple example of concatenating strings.

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

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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). Some modifications of TermExtraction widget were performed later (i.e. the top term cut-offs)

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

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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

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

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

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Example of using Hedwig on a small example.

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This is a copy of the HinMine methodology workflow applied on the IMDB data set.

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Extracted ICCC terminology

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Example of using Aleph on the Michalski trains dataset.

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Example of using Aleph on the Michalski trains dataset.

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

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Attempting to use RSD for feature construction.

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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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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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teste

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Description

Workflow for sentiment analysis of Italian texts. It takes csv file (first row should be column names) as an input and returns csv file with extra column containing sentiment labels for texts. It also shows some stats about the chosen csv column.

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Identify the natural group in a data set

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Data flow diagram

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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

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

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

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

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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

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

Alignment of Slovenian and English terminology extracted from the comparable corpus. For terminology alignment, we use an adaptation of the approach proposed by Aker(2013)

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Description

Alignment of Slovenian and English terminology extracted from the comparable corpus. For terminology alignment, we use an adaptation of the approach proposed by Aker(2013)

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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

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). For terminology alignment, we use an adaptation of the approach proposed by Aker(2013)

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Description

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

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Description

Experiment Description

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Description

Word cloud of virtual reality articles

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Description

Hello

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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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