Purpose

Problem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association.

Design/methodology/approach

This paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations.

Findings

This paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends.

Originality/value

This paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.

中文翻译：

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使用词类比提取科学领域“解决问题”知识图谱的实体关系

目的

“解决问题”是科学研究最重要的关键洞察。本研究重点通过提取四种实体关系类型：问题解决、问题层次、解决方案层次和关联，构建科学领域的“问题解决”知识图谱。

设计/方法论/途径

本文提出了一种基于词语类比来识别科学论文中这些关系的低成本方法。问题解决和层次关系表示为头实体和尾实体的偏移向量，然后通过引用一小组预定义实体关系进行分类。

发现

本文利用Web of Science上的人工智能论文进行了实验，取得了良好的效果。实体关系类型问题层次、问题解决和解决方案层次的F1分分别为0.823、0.815和0.748。本文以计算机视觉为例，展示了提取的关系在构建领域知识图谱和揭示历史研究趋势中的应用。

原创性/价值

本文采用的方法高效且具有良好的泛化能力。它不需要依赖大规模的人工标注语料库，只需要一小部分可以轻松从外部知识资源中提取的实体关系。