MedGraph：一种基于知识图谱嵌入的生物医学引文语义信息检索方法
MedGraph: An experimental semantic information retrieval method using knowledge graph embedding for the biomedical citations indexed in PubMed
摘要：
本文研究了生物医学数字图书馆中的语义搜索问题。首先，我们介绍MedGraph，一个知识图谱嵌入为基础的方法，提供语义相关性检索和排名的生物医学文献索引在PubMed。其次，我们使用PubMed的最佳匹配算法评估我们的方法。此外，我们比较我们的方法MedGraph传统的TFIDF算法。我们使用从PubMed中提取的数据集，包括3000万篇文章的元数据，如摘要，作者信息，引用信息和提取的生物实体提及。我们通过拉取数据集的一个子集来使用预定义的查询和真实排名结果来评估MedGraph。据我们所知，这种技术在生物医学信息检索中还没有被探索过。此外，我们的研究结果提供的证据表明，语义的方法来搜索和相关性的生物医学数字图书馆，依赖于知识图谱建模提供更好的搜索相关性的结果相比，传统的方法在客观的指标。
英文摘要：
Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our method using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TFIDF based algorithm. We use a dataset extracted from PubMed, including 30 million articles' metadata such as abstracts, author information, citation information, and extracted biological entity mentions. We do that by pulling a subset of the dataset to evaluate MedGraph using predefined queries with ground truth ranked results. To our knowledge, this technique has not been explored before in biomedical information retrieval. In addition, our results provide evidence that semantic approaches to search and relevance in biomedical digital libraries that rely on knowledge graph modeling offer better search relevance results when compared with traditional approaches in terms of objective metrics.
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