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.

翻译：我们通过将作者身份识别问题建模为知识图谱构建与精炼过程来加以研究。为此，我们将该问题转化为在人工引导下学习概率逻辑模型（基于知识的学习）。具体而言，我们采用函数梯度提升方法学习关系回归树，从而生成可解释的规则。为融入人类知识，我们注入以一级子句形式呈现的指导建议，对决策树进行精炼。我们在七个作者领域从定量与定性两个维度验证了人类知识的有效性。