Author(s): Kenneth Clarkson, Anna Lisa Gentile, Daniel Gruhl, Petar Ristoski, Joseph Terdiman, Steve Welch
Abstract: Ontologies are a basic tool to formalize and share knowledge. However, very
often the conceptualization of a specific domain depends on the particular
user’s needs. We propose a methodology to perform user-centric ontology population that efficiently includes human-in-the-loop at each step. Given the existence of suitable target ontologies, our methodology supports the alignment of concepts in the user’s conceptualization with concepts of the target ontologies, using a novel hierarchical classification approach. Our methodology also helps the user to build, alter and grow their initial conceptualization, exploiting both the target ontologies and new facts extracted from unstructured data.
We evaluate our approach on a real-world example in the healthcare domain,
in which adverse phrases for drug reactions, as extracted from user blogs, are aligned with MedDRA concepts. The evaluation shows that our approach has high efficacy in assisting the user to both build the initial ontology (HITS@10 up to 99.5%) and to maintain it (HITS@10 up to 99.1%).
Keywords: human-in-the-loop; neural network; ontology alignment