In this study, we present a comprehensive investigation into the mapping of English Wordnet to Wikidata, focusing on the existing mappings created by different projects. We systematically analyze the current mapping methodologies and their effectiveness, highlighting the strengths and limitations of each approach. Through a comparative analysis, we identified overlaps and discrepancies among the mappings, revealing insights into the relationships between the data sets. Our findings underscore the need for a more unified dataset that consolidates disparate mappings into a comprehensive unified Wordnet-Wikidata mapping. We propose a novel construction methodology for this unified data set, taking advantage of existing mappings while addressing their shortcomings. In addition, we discuss future perspectives and advanced techniques for mapping the remaining unmapped records, such as machine learning algorithms. This work not only contributes to the enhancement of data interoperability between Wordnet and Wikidata but also sets the stage for future research aimed at refining mapping techniques and expanding coverage.

The Open Multilingual Wordnet (OMW) is an open source project that was launched with the goal to make it easy to use wordnets in multiple languages without having to pay expensive proprietary licensing costs. As OMW evolved, the interlingual indicator (ILI)1 was used to allow semantically equivalent synsets in different languages to be linked to each other. OdeNet2 is the German language wordnet which forms part of the OMW project. This paper analyses the shortcomings of the initial ILI classification in OdeNet and the consequent methods used to improve this classification.