A collective approach to ranking entities for mentions
A collective approach to ranking entities for mentions
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DOI：
10.1109/icis.2016.7550859
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Shunlin Rong;M. Iwaihara
中科院分区：
文献类型：
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作者：
Shunlin Rong;M. Iwaihara
实体链接（EL）是将web文本中的名称提及映射到知识库中的实体的任务。大多数基于知识方法的早期EL工作通常被表述为排序问题，要么通过(i)带有监督模型的非集体方法，要么通过（ii）利用全局主题一致性的集体方法，这意味着通过基于图的方法实现实体之间的语义关系。对于映射过程，我们可以把它看作是将这两种方法结合起来选择一个实体。在本文中，我们提出了一个概率模型，该模型通过使用三种类型的数据来定制相关实体的排名：实体的流行度知识、提及与实体之间的上下文相似性以及映射实体之间的语义关系。具体来说，我们首先提出了一个利用全局主题一致性的EL模型，这意味着实体之间的语义相关性，以及使用局部提及-实体兼容性，以提高召回率和准确性。该模型的主要优点在于：1)结合两种方法为提及提供定制排序；2)通过全局语义一致性高效地找到候选实体组合，节省了大量的计算量。
Entity linking (EL) is the task of mapping name mentions in web text to their entities in a knowledge base. Most of earlier EL work in the knowledge based approach is usually formulated as a ranking problem, either by (i) non-collective approaches with supervised models, or (ii) collective approaches by leveraging global topical coherence which means semantic relations between entities through graph-based approaches. For the mapping process, we can regard it as selecting an entity to its mention by combining these two methods. In this paper, we propose a probabilistic model that ranks related entities to name mentions where ranking is customized by using three types of data: popularity knowledge of the entity, context similarity between mentions and the entity, and semantic relations between mapping entities. Specifically, we first propose an EL model utilizing global topical coherence that means semantic relatedness between entities, as well as using local mention-to-entity compatibility, to improve recall and precision. The key benefit of our model comes from 1) combination of two methods to provide customized ranking for mentions, 2) the model can save a large amount of calculation by efficiently finding candidate combinations of entities through global semantic coherence.