Ontology-Grounded Topic Modeling for Climate Science Research
Links to Fileshttps://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research
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Type of Work12 pages
conference slides and paper pre-print
Citation of Original PublicationJennifer Sleeman, Tim Finin, Milton Halem, Ontology-Grounded Topic Modeling for Climate Science Research, Emerging Topics in Semantic Technologies. ISWC 2018 Satellite Events, https://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research
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In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases, improves the topics generated and makes them easier to understand. We apply and evaluate this method to the climate science domain. The result improves the topics generated and supports faster research understanding, discovery of social networks among researchers, and automatic ontology generation.