Towards intelligent geospatial data discovery: a machine learning framework for search ranking
Towards intelligent geospatial data discovery: a machine learning framework for search ranking
复制标题
迈向智能地理空间数据发现：用于搜索排名的机器学习框架
DOI：
10.1080/17538947.2017.1371255
复制
发表时间：
2018
影响因子：
5.1
通讯作者：
Christopher J. Finch
中科院分区：
文献类型：
--
作者：
Yongyao Jiang;Yun Li;C. Yang;F. Hu;E. Armstrong;Thomas S. Huang;D. Moroni;L. McGibbney;Christopher J. Finch
摘要当前大多数地理空间数据门户中的搜索引擎倾向于引导用户关注一个单一的数据特征维度(例如流行度和发布日期)。这种方法在很大程度上没有考虑用户对地理空间数据的多维偏好，因此可能导致在发现最适用的数据集方面不是最优的用户体验。通过(1)通过考虑语义、用户行为、空间相似性和静态数据集元数据属性来识别地理空间数据的多个排名特征来表示用户的多维偏好；(2)应用机器学习方法来自动学习排名函数；(3)提出了一个结合现有面向搜索的开源软件、语义知识库、排名特征提取和机器学习算法的系统架构。结果表明，机器学习方法在K处的精度和归一化折扣累积收益方面都优于其他方法。作为利用机器学习来提高地理空间领域搜索排名的初步尝试，我们期待这项工作将为进一步的研究树立榜样，并为智能地理空间数据发现打开大门。
ABSTRACT Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date). This approach largely fails to take account of users’ multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset. This study reports a machine learning framework to address the ranking challenge, the fundamental obstacle in geospatial data discovery, by (1) identifying a number of ranking features of geospatial data to represent users’ multidimensional preferences by considering semantics, user behavior, spatial similarity, and static dataset metadata attributes; (2) applying a machine learning method to automatically learn a ranking function; and (3) proposing a system architecture to combine existing search-oriented open source software, semantic knowledge base, ranking feature extraction, and machine learning algorithm. Results show that the machine learning approach outperforms other methods, in terms of both precision at K and normalized discounted cumulative gain. As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain, we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.