Taxonomy Learning System
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A Taxonomy Learning System is a Machine Learning System that learns different types of taxonomies.
- AKA: Learning Taxonomies, Taxonomy Induction System.
- Example(s):
- Counter-Example(s):
- See: Terminology, Taxonomy.
References
2020
- (iTeachU) ⇒ https://iteachu.uaf.edu/learning-taxonomies/ Retrieved:2020-10-25.
- QUOTE: Most of us agree that our purpose as instructors is to foster or facilitate new student understandings. But, what are understandings? Are there different kinds? It seems they can be simple, such as remembering one’s times tables, or extremely complicated, such as new knowledge about oneself. Several scholars have tried to create taxonomies of understandings, or taxonomies of educational goals. Perhaps the most well known is Bloom’s Taxonomy of the Cognitive domain (1956) later revised by Anderson (2001). “This Taxonomy Features the Recognizable Hierarchy of Categories Which Attempts to Capture the Spectrum of Learning Processes: Remember, Understand, Apply, Analyze, Evaluate, Create.” Anderson extended these categories with the addition of a knowledge dimension: Factual, Conceptual, Procedural, Metacognitive.
2016
- (Espinosa-Anke et al., 2016) ⇒ Luis Espinosa-Anke, Horacio Saggion, Francesco Ronzano, and Roberto Navigli (2016, February). "Extasem! Extending, Taxonomizing and Semantifying Domain Terminologies". In: Proceedings of the 30th conference on artificial intelligence (AAAI’16).
- QUOTE: In terms of taxonomy evaluation, EXTASEM! is able to reliably reconstruct gold standard taxonomies of interdisciplinary domains such as Science, Terrorism or Artificial Intelligence, as well as more specific ones like Food or Equipment. In addition, it has the capacity to extend and semantify an input taxonomy, i.e. increase its size and link many of its nodes to a reference sense inventory.
2013
- (Velardi et al., 2013) ⇒ Paola Velardi, Stefano Faralli and Roberto Navigli (2013). "OntoLearn Reloaded: A Graph-Based Algorithm for Taxonomy Induction". In: Computational Linguistics, 39(3), 665-707.
- QUOTE: In this paper we deal with the problem of learning a taxonomy (i.e., the backbone of an ontology) entirely from scratch. Very few systems in the literature address this task. OntoLearn (Navigli and Velardi 2004) was one of the earliest contributions in this area. In OntoLearn taxonomy learning was accomplished in four steps: terminology extraction, derivation of term sub-trees via string inclusion, disambiguation of domain terms using a novel Word Sense Disambiguation algorithm, and combining the sub-trees into a taxonomy.
2004
- (Navigli & Velardi, 2004) ⇒ Roberto Navigli, and Paola Velardi (2004). Learning domain ontologies from document warehouses and dedicated websites. In: Computational Linguistics, 30(2):151–179
2001
- (Anderson et al., 2001) ⇒ Lorin W. Anderson, David R. Krathwohl, Peter W. Airasian, Kathleen A. Cruikshank, Richard E. Mayer, Paul R. Pintrich, James Raths, and Merlin C. Wittrock. (2001). "A taxonomy for learning, teaching and assessing: A revision of Bloom’s Taxonomy of educational outcomes: Complete edition". New York, NY: Pearson.
1956
- (Bloom, 1956) ⇒ Benjaman S. Bloom (1956). "Taxonomy of educational objectives: the classification of educational goals. Handbook I: cognitive domain". In: New York: David McKay Company. Inc.(7th Edition 1972).