Entity Retrieval
Entity Retrieval
复制标题
实体检索
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Krisztian Balog
中科院分区：
文献类型：
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作者：
Krisztian Balog
.在这项研究中，我们改进了目前的艺术实体检索的结果列表使用图嵌入重新排名。本文表明，图嵌入是有用的面向实体的搜索任务。我们的经验表明，编码信息从知识图到（图）嵌入有助于更高的增加实体检索结果的效率比使用普通字嵌入。我们分析了实体链接器的准确性对整体检索效率的影响。我们的分析进一步部署集群假设来解释观察到的优势，图嵌入更广泛使用的词嵌入，用户任务涉及排名实体。
. 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.