Answer Set Programming on Expert Feedback to Populate and Extend Dynamic Ontologies

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The next generation of online reference works will require structured representations of their contents in order to support scholarly functions such as semantic search, automated generation of cross-references, tables of contents, and ontology-driven conceptual navigation. Many of these works can be expected to contain massive amounts of data and be updated dynamically, which limits the feasibility of ``manually'' coded ontologies to keep up with changes in content. However, relationships relevant to inferring an ontology can be recovered from statistical text processing, and these estimates can be verified with carefully-solicited expert feedback. In this paper, we explain a method by which we have used answer set programming on such expert feedback to dynamically populate and partially infer an ontology for a well-established, open-access reference work, the Stanford Encyclopedia of Philosophy.