Knowledge-based Refinement of Scientific Publication Knowledge Graphs
This work addresses authorship attribution for researchers and publishers, but it is incremental as it builds on existing knowledge-based learning methods.
The authors tackled the problem of identifying authorship by constructing and refining knowledge graphs using a probabilistic logic model with human guidance, demonstrating improved performance across seven authorship domains.
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.