  Software-related platforms have enabled their users to collaboratively label
software entities with topics. Tagging software repositories with relevant
topics can be exploited for facilitating various downstream tasks. For
instance, a correct and complete set of topics assigned to a repository can
increase its visibility. Consequently, this improves the outcome of tasks such
as browsing, searching, navigation, and organization of repositories.
Unfortunately, assigned topics are usually highly noisy, and some repositories
do not have well-assigned topics. Thus, there have been efforts on recommending
topics for software projects, however, the semantic relationships among these
topics have not been exploited so far.
  We propose two recommender models for tagging software projects that
incorporate the semantic relationship among topics. Our approach has two main
phases; (1) we first take a collaborative approach to curate a dataset of
quality topics specifically for the domain of software engineering and
development. We also enrich this data with the semantic relationships among
these topics and encapsulate them in a knowledge graph we call SED-KGraph.
Then, (2) we build two recommender systems; The first one operates only based
on the list of original topics assigned to a repository and the relationships
specified in our knowledge graph. The second predictive model, however, assumes
there are no topics available for a repository, hence it proceeds to predict
the relevant topics based on both textual information of a software project and
SED-KGraph.
  We built SED-KGraph in a crowd-sourced project with 170 contributors from
both academia and industry. The experiment results indicate that our solutions
outperform baselines that neglect the semantic relationships among topics by at
least 25% and 23% in terms of ASR and MAP metrics.
