Weakly Supervised Approaches for Ontology Population

We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsupervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories geographical locations and person names and ten sub-classes for each category. For each sub-class, from a list of training examples and a syntactically parsed corpus, we automatically learn a syntactic model a set of weighted syntactic features, i.e. words which typically co-occur in certain syntactic positions with the members of that class. The model is then used to classify the unknown Named Entities in the test set. The method is weakly supervised, since no annotated corpus is used in the learning process. We achieved promising results, i.e. 65% accuracy, outperforming significantly previous unsupervised approaches.