顾及地理语境的旅游轨迹停留点语义标注
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摘要
轨迹数据作为典型的时空大数据,具有较高的研究和应用价值。然而现有的轨迹数据挖掘主要聚焦于轨迹的空间特征,而较少关注轨迹数据语义的深度分析。本文面向智慧旅游服务的需求,重点探讨了旅游轨迹的轨迹停留点语义的自动标注问题。首先,针对POI短文本的特点,提出了基于《同义词词林》进行短文本语义扩展的方法对POI短文本进行特征扩展;同时,在顾及POI短文本的关键词集中、类别词分散等特征基础上,提出了改进TF-IDF的POI自动分类方法;其次,在POI分类的基础上,采用Native Bayes方法对轨迹停留点进行语义标注。结果表明,基于改进TF-IDF方法的POI自动分类可以达到约83%的精度,能够较好地实现POI的分类;而在POI自动分类基础上,基于Native Bayes的轨迹语义标注可以达到74%的精度,较好地实现了旅游轨迹停留点自动语义标注的目标。
As a typical space-time big data,trajectory data has high research and application value. However,the existing trajectory data mining mainly focuses on the spatial features of the trajectory,and less on the depth analysis of the trajectory data semantics. This paper is oriented to the demand of smart travel service,aiming at the semantic annotation of the track stop point,focusing on the automatic labeling problem of the track stay point semantics of the travel track. Firstly,aiming at the characteristics of short text of POI,this paper proposes a method of semantic extension of short text based on "synonym word forest"to extend the feature of POI short text. At the same time,it takes into account the feature set of POI short text and the distribution of category words. In this paper,the POI automatic classification method for improving TF-IDF is proposed. Secondly,based on the POI classification,the Native Bayes method is used to semantically mark the track stay points. The results show that the automatic classification of POI based on the improved TF-IDF method can achieve about 83% accuracy,which can better realize the classification of POI. On the basis of automatic classification of POI,the trajectory semantic annotation based on Native Bayes can reach 74%. The accuracy of the target is automatically achieved by the automatic semantic annotation of the travel track stay point.
As a typical space-time big data,trajectory data has high research and application value. However,the existing trajectory data mining mainly focuses on the spatial features of the trajectory,and less on the depth analysis of the trajectory data semantics. This paper is oriented to the demand of smart travel service,aiming at the semantic annotation of the track stop point,focusing on the automatic labeling problem of the track stay point semantics of the travel track. Firstly,aiming at the characteristics of short text of POI,this paper proposes a method of semantic extension of short text based on "synonym word forest"to extend the feature of POI short text. At the same time,it takes into account the feature set of POI short text and the distribution of category words. In this paper,the POI automatic classification method for improving TF-IDF is proposed. Secondly,based on the POI classification,the Native Bayes method is used to semantically mark the track stay points. The results show that the automatic classification of POI based on the improved TF-IDF method can achieve about 83% accuracy,which can better realize the classification of POI. On the basis of automatic classification of POI,the trajectory semantic annotation based on Native Bayes can reach 74%. The accuracy of the target is automatically achieved by the automatic semantic annotation of the travel track stay point.
引文
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[3]LEE J G,HAN J,WHANG K Y.Trajectory clustering:a partition-and-group framework[C]∥Proceedings of SIGMOD.Beijing:ACM,2007.
[4]杨林,左泽均,叶亚琴,等.基于浮动车轨迹数据的路口车道结构求解[J].测绘科学,2014,39(12):76-80.
[5]杨敏,陈媛媛,金澄,等.保持移动速度特征的轨迹线化简方法[J].测绘学报,2017,46(12):2016-2023.
[6]ZHENG Y,ZHANG L,XIE X,et al.Mining interesting locations and travel sequences from GPS trajectories[C]∥Proceedings of the 18th International Conference on WWW.Modrid:DBLP,2009.
[7]向隆刚,龚健雅,吴涛,等.一种面向Stop/Move抽象的轨迹时空关系[J].武汉大学学报(信息科学版),2014,39(8):956-962.
[8]连富德,张富峥,王英子,等.移动数据挖掘[M].北京:机械工业出版社,2017.
[9]YAN Z,CHAKRABORTY D,PARENT C,et al.Semantic trajectories:mobility data computation and annotation[J].ACM Transactions on Intelligent Systems&Technology,2013,4(3):49.
[10]崔邓,沈敬伟,周廷刚.一种面向移动智能手机用户轨迹停留点提取的时空聚类算法研究[J].西南师范大学学报(自然科学版),2016,41(10):81-87.
[11]PALMA A T,BOGORNY V,KUIJPERS B,et al.Aclustering-based approach for discovering interesting places in trajectories[C]∥Proceedings of ACM SAC.Fortaleza:ACM,2008.
[12]ZIMMERMANN M,KIRSTE T,SPILIOPOULOU M.Finding stops in error-prone trajectories of moving objects with time-based clustering[C]∥Proceedings of IMC 2009.Rostock-Warnemiinde:[s.n.],2009.
[13]ZHENG Y.Trajectory data mining:an overview[M].[S.l.]:ACM,2015:1-41.
[14]HUANG G,HE J,ZHOU W,et al.Discovery of stop regions for understanding repeat travel behaviors of moving objects[J].Journal of Computer&System Sciences,2016,82(4):582-593.
[15]王荣波,谌志群,周建政,等.基于Wikipedia的短文本语义相关度计算方法[J].计算机应用与软件,2015,32(1):82-85.
[16]张思琪,邢薇薇,蔡圆媛.一种基于Word Net的混合式语义相似度计算方法[J].计算机工程与科学,2017,39(5):971-977.
[17]CHEN Q,YAO L,YANG J.Short text classification based on LDA topic model[C]∥Proceedings of 2016ICALIP.Shanghai:IEEE,2016.
[18]黄发良,冯时,王大玲,等.基于多特征融合的微博主题情感挖掘[J].计算机学报,2017,40(4):872-888.
[19]吕志娟.基于Lifelog数据的个人轨迹模式挖掘算法的研究与应用[D].沈阳:东北大学,2015.