利用地点和评论上的关联图,寻找适合某些目的的地点的地理实体检索

J. Data Intell. Pub Date : 1900-01-01 DOI:10.26421/jdi3.4-1
Yui Maekawa, Yoshiyuki Shoji, M. Dürst
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引用次数: 0

摘要

本文提出了一种对地理实体(地点)进行排序的方法,其中以查询形式给出的目的可以实现。大多数现有的地图搜索引擎只接受地名或地名类型。因此,当搜索者想要找到一个适合“吉他练习”的地方时,他们必须输入一个地方类型,比如“音乐工作室”。要创建这样的查询,需要先验知识(\textit{例如},音乐工作室适合弹吉他)。我们提出的方法使用关于位置的在线评论信息,以支持从给定目的查询中直接检索位置。我们的方法创建了一个由地点和这些地点的评论中出现的单词组成的二部图。给定关键字查询和位置之间的相关性是通过使用随机行走与重新启动算法来计算的。此外,我们用三个假设展开图:1)适合相同目的的地方彼此相似,并且可以在同一地方实现的目的彼此相似;2)具有相似元数据的地方可以实现相同的目的;3)具有语义相似含义的目的可以在相同的地方实现。通过使用谷歌地图上的真实评论数据进行实验,验证了该方法的有效性。实验结果表明,按地点元数据进行扩展可以有效地找到更相关的地点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Geographic Entity Retrieval for Finding Places Suitable for Certain Purposes by Using Relevance Graphs on Places and Reviews
This paper proposes a method of ranking geographic entities (places) where a purpose, given as a query, can be achieved. Most existing map search engines accept only the name of a place or the type of a place. Thus when searchers want to find a suitable place for ``guitar practice'', they have to input a place type such as ``music studio''. To create such a query, prior knowledge (\textit{i.e.}, that a music studio is suitable for playing guitar) is required. Our proposed method uses online review information on places to enable direct place retrieval from a given purpose query. Our method creates a bipartite graph consisting of places and the words that appear in the reviews of these places. The relevance between the given keyword query and a place is calculated by using the Random Walk with Restart algorithm. Additionally, we expand the graph with three hypotheses: 1) places that are suitable for the same purpose are similar to each other, and purposes that can be achieved in the same place are similar to each other, 2) the same purpose can be achieved in places with similar metadata, and 3) purposes which have semantically similar meaning can be achieved in the same places. Through an experiment using real review data taken from Google Maps, the usefulness of the proposed method was demonstrated. In particular, experimental result shows that the expansion by places' metadata is effective for finding more relevant places.
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