So far away and yet so close: augmenting toponym disambiguation and similarity with text-based networks
So far away and yet so close: augmenting toponym disambiguation and similarity with text-based networks
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如此遥远却又如此接近：通过基于文本的网络增强地名消歧和相似性
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Michael Gertz
中科院分区：
文献类型：
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作者：
Andreas Spitz;Johanna Geiß;Michael Gertz
地点相似性在地理信息检索和地理信息系统中起着核心作用，在地理信息系统中，空间接近往往只是语义相关的一个很差的替代品。因此，对于诸如地名歧义消除之类的应用，需要采用替代措施来回答给定上下文中的地点相似性这一重要问题。在本文中，我们讨论了一种从非结构化文本数据中构建位置网络的新方法。通过基于地名的文本距离计算相似度，我们得到了一种关联度，该关联度编码了地点提及的共现的重要性。基于英文维基百科的文本，我们构建并提供了这样一个地点相似性网络，包括链接到维基数据的实体作为所包含信息的补充。在中心性分析中，我们探索了网络捕捉地点之间相似性的能力。对该网络在AIDA CoNLL-Yago数据集上的地名消歧任务进行的评估表明，该网络的性能与最先进的方法一致。
Place similarity has a central role in geographic information retrieval and geographic information systems, where spatial proximity is frequently just a poor substitute for semantic relatedness. For applications such as toponym disambiguation, alternative measures are thus required to answer the non-trivial question of place similarity in a given context. In this paper, we discuss a novel approach to the construction of a network of locations from unstructured text data. By deriving similarity scores based on the textual distance of toponyms, we obtain a kind of relatedness that encodes the importance of the co-occurrences of place mentions. Based on the text of the English Wikipedia, we construct and provide such a network of place similarities, including entity linking to Wikidata as an augmentation of the contained information. In an analysis of centrality, we explore the networks capability of capturing the similarity between places. An evaluation of the network for the task of toponym disambiguation on the AIDA CoNLL-YAGO dataset reveals a performance that is in line with state-of-the-art methods.
DOI：
10.1145/2872518.2891109
发表时间：
2016-04
期刊：
Proceedings of the 25th International Conference Companion on World Wide Web
影响因子：
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作者：
Johanna Geiß;Michael Gertz
通讯作者：
Johanna Geiß;Michael Gertz