Abstract

In recent years, several ontological resources have been proposed to model machine learning domain. However, they do not provide a direct link to linguistic data. In this paper, we propose a linguistic resource, a set of several semantic frames with associated annotated initial corpus in machine learning domain, we coined MLFrameNet. We have bootstrapped the process of (manual) frame creation by text mining on the set of 1293 articles from the Machine Learning Journal from about 100 volumes of the journal. It allowed us to find frequent occurences of words and bigrams serving as candidates for lexical units and frame elements. We bridge the gap between linguistics analysis and formal ontologies by typing the frame elements with semantic types from the DMOP domain ontology. The resulting resource is aimed to facilitate tasks such as knowledge extraction, question answering, summarization etc. in machine learning domain.

Date

September 22, 2025

Authors

Heiko Paulheim, Jens Lehmann, Vojtech Svatek, Craig Knoblock, Matthew Horridge, Patrick Lambrix, Bijan Parsia

Conference

1st International Workshop on Completing and Debugging the Semantic Web, Heraklion, Greece, May 30th, 2016

Publisher

Rheinisch-Westfaelische Technische Hochschule Aachen University