Ontology Learning from Text Using Automatic Ontological-Semantic Text Annotation and the Web as the Corpus

We present initial experimental results of an approach to learning ontological concepts from text. For each word to be learned, our system a) creates a corpus of sentences, derived from the web, containing this word; b) automatically semantically annotates the corpus using the OntoSem semantic analyzer; c) creates a candidate new concept by collating semantic information from annotated sentences; and d) finds in the existing ontology concept(s) “closest” to the candidate. In the long term, our approach is intended to support the continual mutual bootstrapping of the learner and the semantic analyzer as a solution to the knowledge acquisition bottleneck problem in AI.