A Confidence-Weighted Metric for Unsupervised Ontology Population from Web Texts

Knowledge engineers have had difficulty in automatically constructing and populating domain ontologies, mainly due to the well-known knowledge acquisition bottleneck. In this paper, we attempt to alleviate this problem by proposing an unsupervised approach for extracting class instances using the web as a big corpus and exploring linguistic patterns to identify and extract ontological class instances. The prototype implementation uses shallow syntactic parsing for disambiguation issues. In addition, we propose a confidence-weighted metric based on different versions of the classical PMI metric, WordNet similarity measures, and heuristics to calculate the final confidence score that can altogether improve the ranking of candidate instances retrieved by the system. We conducted preliminary experiments comparing the proposed confidence metric against some versions of the PMI metric. We obtained promising results for the final ranking of the candidate instances, achieving a gain in precision up to 24%.