Knowledge-based Refinement of Scientific Publication Knowledge Graphs
Siwen Yan Phillip Odom Sriraam Natarajan
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.