Targeting criteria in online advertising differ across platforms and
frequently change. Because advertisers are increasingly taking a
multi-channel approach to online marketing, there is a need to
automatically map the targeting criteria between ad platforms. In this
research, we test two algorithmic approaches  Word2Vec and WordNet 
for mapping ad targeting criteria between Google Ads and Facebook Ads.
The results show that Word2Vec outperforms WordNet in finding matches
(97.5% vs. 63.6%), covering different criteria (20.0% vs. 13.5%), and
having higher similarity scores. However, WordNet outperforms Word2Vec
in expert evaluation (Mean Opinion Score = 3.05 vs. 2.46), implying that
algorithmic performance metrics may not correlate with expert ratings.
Overall, due to specific requirements for mapping ad targeting criteria,
automatic means do not (at least yet) offer a satisfactory solution for
replacing human judgment.