Supervised identification and linking of concept mentions to a domain-specific ontology

Abstract

We propose a pipelined supervised learning approach named SDOI to the task of interlinking the concepts mentioned within a document to the concepts within an ontology. Concept mention identification is performed by training a sequential tagging model. Each identified concept mention is then associated with a set of candidate ontology concepts along with a feature vector based on features proposed in the literature and novel ones based on new data sources, such as from the training corpus itself. An iterative algorithm is defined for handling collective features. We show a lift in performance over applicable baselines against the ability to identify the concept mentions within the 139 KDD-2009 conference paper abstracts, and to link these concept mentions to a domain-specific ontology for the field of data mining. Additional experiments of 22 ICDM-2009 abstracts suggest that the trained models are portable both in terms of accuracy and in their ability to reduce annotation time.