对比多种图嵌入与实体链接方法，提升实体检索效果。
Graph-Embedding Empowered Entity Retrieval
- 用嵌入距离对候选实体重排序，融合图结构与文本描述。
- 结合结构与文本的嵌入方法表现最佳，需覆盖尽可能多实体。
- 高精度和高召回的实体链接对检索效果至关重要。
本文研究基于图嵌入的实体检索方法。尽管已有多种方法被提出，但多数仅采用单一图嵌入与实体链接方案，限制了对不同方法组合效果的理解。为此，我们评估了三类图嵌入技术与五种实体链接方法的影响。通过计算标注实体与待重排序实体之间的嵌入距离进行重排序。结果表明，图嵌入与实体链接器的选择均显著影响检索效果：结合图结构与实体文本描述的嵌入方法最有效；实体链接需兼顾概念层面的精确率与召回率；同时，图谱应尽可能包含更多实体以提升性能。
原文摘要 · Abstract (English)
In this research, we investigate methods for entity retrieval using graph embeddings. While various methods have been proposed over the years, most utilize a single graph embedding and entity linking approach. This hinders our understanding of how different graph embedding and entity linking methods impact entity retrieval. To address this gap, we investigate the effects of three different categories of graph embedding techniques and five different entity linking methods. We perform a reranking of entities using the distance between the embeddings of annotated entities and the entities we wish to rerank. We conclude that the selection of both graph embeddings and entity linkers significantly impacts the effectiveness of entity retrieval. For graph embeddings, methods that incorporate both graph structure and textual descriptions of entities are the most effective. For entity linking, both precision and recall concerning concepts are important for optimal retrieval performance. Additionally, it is essential for the graph to encompass as many entities as possible.
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