Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing, however, their focus was more on semantics than on graph structure. This study, by contrast, takes a special interest in exploring the structural patterns of knowledge graphs. Inspired by human cognitive processes, we propose novel methods of feature extraction for capturing the local and global structural information of knowledge graphs. These features not only exhibit good indicative power, but can also facilitate text analysis with explainable meanings. Moreover, aiming to better combine structural and semantic evidence for prediction, we propose a Neural Knowledge Graph Evaluator (NKGE) which showed superior performance over existing methods. Our contributions include a novel set of interpretable structural features and the effective NKGE for compatibility evaluation between knowledge graphs. The methods of feature extraction and the structural patterns indicated by the features may also provide insights for related studies in computational modeling and processing of knowledge.

有效地检测答案的支持知识是迈向自动提问的基本步骤。尽管针对文本的预训练语义矢量已启用了背景答案对的语义计算，但它们在表示与问题解答相关的结构化知识方面受到限制。最近的研究表明，有兴趣注册用于文本处理的结构化知识图，但是，他们的重点更多地放在语义上，而不是图结构上。相比之下，这项研究对探索知识图的结构模式特别感兴趣。受人类认知过程的启发，我们提出了特征提取的新方法，用于捕获知识图的局部和全局结构信息。这些功能不仅具有良好的指示力，但也可以促进具有可解释含义的文本分析。此外，为了更好地结合结构和语义证据进行预测，我们提出了一种神经知识图评估器（NKGE），该评估器表现出优于现有方法的性能。我们的贡献包括一套新颖的可解释的结构特征以及有效的NKGE，用于评估知识图之间的兼容性。特征提取的方法和特征指示的结构模式还可以为有关计算建模和知识处理的相关研究提供见识。我们的贡献包括一套新颖的可解释的结构特征和有效的NKGE，用于评估知识图之间的兼容性。特征提取的方法和特征指示的结构模式还可以为有关计算建模和知识处理的相关研究提供见识。我们的贡献包括一套新颖的可解释的结构特征和有效的NKGE，用于评估知识图之间的兼容性。特征提取的方法和特征指示的结构模式还可以为有关计算建模和知识处理的相关研究提供见识。