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OntoGen: Data-Driven Ontology Construction System

OntoGen is a semi-automatic system for constructing topic ontologies using text-mining methods and interactive features. It offers clustering algorithms for topic suggestions, keyword extraction for concept naming, and active learning for new concept discovery. The system enables ontology visualization, concept hierarchy management, instance classification, and simultaneous ontologies creation. Users can interact with document data, classify new instances, and visualize concepts and instances within the ontology.

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OntoGen: Data-Driven Ontology Construction System

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  1. Semi-Automatic Data-Driven Ontology Construction System Blaz Fortuna, Marko Grobelnik, Dunja Mladenic Jozef Stefan Institute

  2. Main features of OntoGen • Semi-Automatic • Text-mining methods provide suggestions and insights into the domain • The user can interact with parameters of text-mining methods • All the final decisions are taken by the user • Data-Driven • Most of the aid provided by the system is based on some underlying data provided by the system • Instances are described by features extracted from the data (e.g. bag-of-words vectors)

  3. OntoGen v1.0 • Designed for construction of topic ontologies • Clustering algorithms used for topic suggestion • Keyword extractions methods help the user to name the concept • Interactive user interface

  4. OntoGen v2.0 • Improved user interface • Based on the feedback from users • New features: • Active Learning • Learning new concepts based on user queries and user classification of carefully selected documents • Simultaneous Ontologies • Optimization of similarity measure based on provided document categories • Concept’s Instances Visualization • Integration of Document Atlas visualization • Ontology Population • Interactive classification of new instances into ontology

  5. Concept hierarchy Sub-Concept suggestion Ontology visualization

  6. Concept hierarchy Concept’s documents management Selected concept’s details

  7. Active Learning • SVM hyperplane distance based active learning algorithm • First few labelled documents are bootstrapped using user query and nearest-neighbour search • In each step the unlabeled document closest to the hyperplane is chosen for user classification

  8. New Concept

  9. Simultaneous Ontologies Topics view • Data: Reuters news articles • Each news is assigned two different sets of categories: • Topics • Countries • Each set of categories offers a different view on the data Countries view Documents

  10. Concept’s Instances Visualization

  11. Ontology Population • One vs. All linear SVM used classification • Interactive user interface where user can finalize the classifications

  12. New documents Classification of the selected document Selected document

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