发表机构
VSB – Technical University of Ostrava（俄斯特拉发技术大学（VSB））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对学术出版网络分析，提出结合SPECTER2语义嵌入与书目耦合的结构-语义融合方法，通过可调权重适应任务，实验证明其优于TF-IDF并开源实现。
AI 中文摘要
学术出版网络的结构图分析能够捕捉实体之间的拓扑关系，但无法看到作品的内容。在结构方法的基础上，本工作通过语义层和参数化的结构-语义融合对其进行补充。我们使用基于引文的向量嵌入（SPECTER2）表示科学文档，并将其存储在以稳定的OpenAlex ID为键的嵌入向量数据库中，从而直接连接到图结构层。我们定义了一个模块化的后期融合函数，将语义相似度（嵌入的余弦相似度）和结构相似度（书目耦合）与可调权重alpha相结合，该权重根据具体任务选择。在俄斯特拉发技术大学（VSB - Technical University of Ostrava）的语料库上，我们展示了两个方面：基于引文的嵌入比TF-IDF基线更符合专家制定的OpenAlex主题分类；在推荐用例中，结构信号、语义信号和组合信号在不同数据可用性条件下携带不同机制的信息。这里的混合融合并非一种普遍更优的方法，而是一种根据任务引导互补信号的显式机制。我们将整个方法作为apnet库的开源扩展发布，并提供可复现的工作流程。
英文摘要
Structural graph analysis of the academic publishing network captures the topological relationships between entities but does not see the content of works. Building on our structural approach, this work complements it with a semantic layer and a parameterized structural-semantic fusion. We represent scientific documents by citation-informed vector embeddings (SPECTER2) and store them in an embedded vector database keyed by the stable OpenAlex ID, so that they connect directly to the graph layer. We define a modular late-fusion function that combines semantic similarity (cosine of embeddings) and structural similarity (bibliographic coupling) with a tunable weight alpha whose value is chosen according to the specific task. On the corpus of VSB - Technical University of Ostrava we show two things: citation-informed embeddings agree with the expert OpenAlex topical taxonomy better than a TF-IDF baseline, and in a recommendation use case the structural, semantic, and combined signals carry information in different regimes depending on the available data. Hybrid fusion here is not a universally better method but an explicit mechanism for steering complementary signals according to the task. We release the whole approach as an open-source extension of the apnet library with a reproducible workflow.
