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. 2012 Jan 3;7(1):e27499. doi: 10.1371/journal.pone.0027499

Table 2. Comparison of our method with previous works.

Frameworks Main characteristics Main differences in relation to our project
A Flexible Framework to Experiment with Ontology Learning Techniques [18] Semi-automated method using NLP Requires an annotated corpus for entity recognition
A Hybrid Approach for Taxonomy Learning from Text [19] Linguistic patterns associated with statistical reasoning Based on statistical reasoning
Advancing Topic Ontology Learning through Term Extraction [20] Semi-automated based on node extraction Does not make use of collaborative databases for discovery, validation and classification of entities
Automated Ontology Learning and Validation Using Hypothesis Testing [21] Hypothesis-driven Hypotheses are compared against indicators retrieved from the Web
OntoLearn, a methodology for automatic learning of domain ontologies [22] Automated extraction Error rates related to the database, language dependent
Text2Onto - A Framework for Ontology Learning and Data-Driven Change Discovery [23] Probabilistic Ontology Models and identification of change in data patterns Does not require a pre-built ontology