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Using Spreading Activation to Evaluate and Improve Ontologies

In this paper, we explore the relationship between the human-encoded semantics of ontologies and their application to natural language processing (NLP) tasks, such as word-sense disambiguation (WSD), for which such ontologies may not have been originally designed. We present a method for assessing the semantic content of an ontology with respect to a target domain, by spreading activation over a graph that represents instances of ontology concepts and relationships, in domain text. Our proposed method has several advantages beyond existing ontology metrics. By identifying bias or imbalance in the ontology, we can suggest target areas for improvement, and simultaneously facilitate the automated optimisation of the graph for use in the chosen NLP task. On applying this method to the Unified Medical Language System (UMLS) ontology, we significantly outperformed existing graph-based methods for WSD in biomedical NLP (0.82 accuracy). The subsequent introduction of a fall-back mechanism, using word-sense probability, achieved state of the art for unsupervised biomedical WSD (0.89 accuracy).