用图查询提升生物医学文献检索，支持部分匹配与语义扩展。
Ranking Narrative Query Graphs for Biomedical Document Retrieval (Technical Report)
- 将文献视为知识图谱，通过图查询实现精准检索。
- 引入部分匹配与本体重写，召回率和准确率显著提升。
- 适合需要深度关联推理的科研人员使用。
关键词搜索是数字图书馆的主流方式，但在科学知识库等复杂场景中，需更智能的访问路径。每篇文档虽贡献于领域知识，但其内部关键词间的关系与上下文结构对有效检索至关重要。因此，可将单篇文档视为小型知识图谱，利用图查询实现聚焦式检索。本文在已有的生物医学图检索系统基础上，提出新的无监督图排序方法、查询松弛范式及本体重写机制，突破传统‘精确匹配’的局限。新方法通过部分匹配与语义扩展，显著提升检索精度与召回率，使用户能更高效地获取相关文献。
原文摘要 · Abstract (English)
Keyword-based searches are today's standard in digital libraries. Yet, complex retrieval scenarios like in scientific knowledge bases, need more sophisticated access paths. Although each document somewhat contributes to a domain's body of knowledge, the exact structure between keywords, i.e., their possible relationships, and the contexts spanned within each single document will be crucial for effective retrieval. Following this logic, individual documents can be seen as small-scale knowledge graphs on which graph queries can provide focused document retrieval. We implemented a full-fledged graph-based discovery system for the biomedical domain and demonstrated its benefits in the past. Unfortunately, graph-based retrieval methods generally follow an 'exact match' paradigm, which severely hampers search efficiency, since exact match results are hard to rank by relevance. This paper extends our existing discovery system and contributes effective graph-based unsupervised ranking methods, a new query relaxation paradigm, and ontological rewriting. These extensions improve the system further so that users can retrieve results with higher precision and higher recall due to partial matching and ontological rewriting.
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