We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement. To this effect, we model this problem as learning a probabilistic logic model in the presence of human guidance (knowledge-based learning). Specifically, we learn relational regression trees using functional gradient boosting that outputs explainable rules. To incorporate human knowledge, advice in the form of first-order clauses is injected to refine the trees. We demonstrate the usefulness of human knowledge both quantitatively and qualitatively in seven authorship domains.
@article{arxiv.2309.05681,
title = {Knowledge-based Refinement of Scientific Publication Knowledge Graphs},
author = {Siwen Yan and Phillip Odom and Sriraam Natarajan},
journal= {arXiv preprint arXiv:2309.05681},
year = {2023}
}