LOVER: support for modeling data using linked open vocabularies

Various best practices and principles are provided to guide an ontology engineer when modeling Linked Data. The choice of appropriate vocabularies is one essential aspect in the guidelines, as it leads to better interpretation, querying, and consumption of the data by Linked Data applications and users. In this paper, we propose LOVER: a novel approach to support the ontology engineer in modeling a Linked Data dataset. We illustrate the concept of LOVER, which supports the engineer by recommending appropriate classes and properties from existing and actively used vocabularies. The recommendations are made on the basis of on an iterative multimodal search. It uses different, orthogonal information sources for finding vocabulary terms, e.g. based on a best string match or schema information on other datasets published in the Linked Open Data cloud. We describe LOVER's recommendation mechanism in general and illustrate it along a real-life example from the social sciences domain.