Entity linking (EL) systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base. Existing EL approaches usually ignore the semantic correlation between the mentions in the text, and are limited to the scale of the local knowledge base. In this paper, we propose a novel graphranking collective Chinese entity linking (GRCCEL) algorithm, which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources. By improved weighted word2vec textual similarity and improved PageRank algorithm, more semantic information and structural information can be captured in the document. With an incremental evidence mining process, more powerful discrimination capability for similar entities can be obtained. We evaluate the performance of our algorithm on some open domain corpus. Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.

中文翻译：

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图排序集体中文实体链接算法

实体链接（EL）系统旨在将文档中的实体提及链接到参考知识库中的相应实体记录。现有的EL方法通常会忽略文本中提到的内容之间的语义相关性，并且仅限于本地知识库的规模。在本文中，我们提出了一种新颖的图形排序集体中文实体链接（GRCCEL）算法，该算法既可以利用本地知识库中实体之间的结构化关系，又可以利用外部知识源提供的其他背景信息。通过改进的加权word2vec文本相似性和改进的PageRank算法，可以在文档中捕获更多的语义信息和结构信息。通过逐步的证据挖掘过程，可以获得对相似实体更强大的判别能力。我们评估某些开放域语料库上算法的性能。实验结果证明了我们的方法在中文实体链接任务中的有效性，并证明了我们的方法优于最新方法的优越性。