论文标题
图形授权实体检索
Graph-Embedding Empowered Entity Retrieval
论文作者
论文摘要
在这项研究中，我们通过使用图形嵌入将结果列表重新列入结果列表来改善实体检索中的当前状态。本文表明，图形嵌入对于面向实体的搜索任务很有用。我们从经验上证明，与使用普通单词嵌入相比，将信息图编码到（图）嵌入到（图）嵌入到（图）嵌入的有效性更高。我们分析了实体链接器对整体检索有效性的准确性的影响。我们的分析进一步部署了集群假设，以解释图形嵌入的优势比更广泛使用的单词嵌入，用于涉及排名实体的用户任务。
In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities.
