Detecting meaningful compounds in complex class labels

Abstract

Real-world ontologies such as, for instance, those for the medical domain often represent highly specific, fine-grained concepts using complex labels that consist of a sequence of sublabels. In this paper, we investigate the problem of automatically detecting meaningful compounds in such complex class labels to support methods that require an automatic understanding of their meaning such as, for example, ontology matching, ontology learning and semantic search. We formulate compound identification as a supervised learning task and investigate a variety of heterogeneous features, including statistical (i.e., knowledgelean) as well as knowledge-based, for the task at hand. Our classifiers are trained and evaluated using a manually annotated dataset consisting of about 300 complex labels taken from real-world ontologies, which we designed to provide a benchmarking gold standard for this task. Experimental results show that by using a combination of distributional and knowledge-based features we are able to reach an accuracy of more than 90% for compounds of length one and almost 80% for compounds of length two. Finally, we evaluate our method in an extrinsic experimental setting: this consists of a use case highlighting the benefits of using automatically identified compounds for the high-end semantic task of ontology matching.