Knowledge-based Refinement of Scientific Publication Knowledge Graphs

TL;DR

This paper presents a method for refining scientific publication knowledge graphs by integrating human knowledge through probabilistic logic models and relational regression trees, improving authorship identification.

Contribution

It introduces a novel knowledge-based learning approach that incorporates human guidance into probabilistic logic models for knowledge graph refinement.

Findings

Effective in seven authorship domains

Improves accuracy of authorship identification

Demonstrates the value of human knowledge integration

Abstract

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.