Geographic data plays an essential role in various Web, Semantic Web and
machine learning applications. OpenStreetMap and knowledge graphs are critical
complementary sources of geographic data on the Web. However, data veracity,
the lack of integration of geographic and semantic characteristics, and
incomplete representations substantially limit the data utility. Verification,
enrichment and semantic representation are essential for making geographic data
accessible for the Semantic Web and machine learning. This article describes
recent approaches we developed to tackle these challenges.

本文介绍了我们最近开发的方法，以解决地理数据在 Web 和机器学习应用中的数据真实性、缺乏地理和语义特性集成以及不完整表示等问题。

创建 Web 地理数据知识图谱

Creating Knowledge Graphs for Geographic Data on the Web

Twitter has rapidly emerged as one of the largest worldwide venues for
written communication. Thanks to the ease with which vast quantities of tweets
can be mined, Twitter has also become a source for studying modern linguistic
style. The readability of text has long provided a simple method to
characterize the complexity of language and ease that documents may be
understood by readers. In this note we use a modified version of the Flesch
Reading Ease formula, applied to a corpus of 17.4 million tweets. We find
tweets have characteristically more difficult readability scores compared to
other short format communication, such as SMS or chat. This linguistic
difference is insensitive to the presence of "hashtags" within tweets. By
utilizing geographic data provided by 2% of users, joined with "ZIP Code
Tabulation Area" (ZCTA) level education data from the U.S. Census, we find an
intriguing correlation between the average readability and the college
graduation rate within a ZCTA. This points towards a difference in either the
underlying language, or a change in the type of content being tweeted in these
areas

本研究分析了使用 Flesch 阅读难度公式修改后的 17.4 万条推文，发现推文的阅读难度明显高于短信和聊天群组，对于推文阅读难度与地理数据的相关研究发现，与大学毕业率存在着重要相关性，这说明了不同的地区存在着不同的语言差异。