Understanding Emerging Spatial Entities

In Foursquare or Google+ Local, emerging spatial entities, such as new business or venue, are reported to grow by 1% every day. As information on such spatial entities is initially limited (e.g., only name), we need to quickly harvest related information from social media such as Flickr photos. Especially, achieving high-recall in photo population is essential for emerging spatial entities, which suffer from data sparseness (e.g., 71% restaurants of TripAdvisor in Seattle do not have any photo, as of Sep 03, 2015). Our goal is thus to address this limitation by identifying effective linking techniques for emerging spatial entities and photos. Compared with state-of-the-art baselines, our proposed approach improves recall and F1 score by up to 24% and 18%, respectively. To show the effectiveness and robustness of our approach, we have conducted extensive experiments in three different cities, Seattle, Washington D.C., and Taipei, of varying characteristics such as geographical density and language.

[1]  Wei Shen,et al.  LINDEN: linking named entities with knowledge base via semantic knowledge , 2012, WWW.

[2]  Gerhard Weikum,et al.  Finding images of difficult entities in the long tail , 2011, CIKM '11.

[3]  Gerhard Weikum,et al.  Discovering emerging entities with ambiguous names , 2014, WWW.

[4]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[5]  Xueming Qian,et al.  Tagging photos using users' vocabularies , 2013, Neurocomputing.

[6]  Kuansan Wang,et al.  Entity linking at the tail: sparse signals, unknown entities, and phrase models , 2014, WSDM.

[7]  Surajit Chaudhuri,et al.  A framework for robust discovery of entity synonyms , 2012, KDD.

[8]  Rohini K. Srihari,et al.  A Hybrid Approach for Named Entity and Sub-Type Tagging , 2000, ANLP.

[9]  Jianyong Wang,et al.  GRIAS: An Entity-Relation Graph Based Framework for Discovering Entity Aliases , 2013, 2013 IEEE 13th International Conference on Data Mining.

[10]  Yitong Li,et al.  Entity Linking for Tweets , 2013, ACL.

[11]  Harald Kosch,et al.  Geo-based automatic image annotation , 2012, ICMR '12.

[12]  Tat-Seng Chua,et al.  Tour the world: Building a web-scale landmark recognition engine , 2009, CVPR.

[13]  Jussara M. Almeida,et al.  Associative tag recommendation exploiting multiple textual features , 2011, SIGIR.

[14]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[15]  Bruno Martins,et al.  Tag recommendation for georeferenced photos , 2011, LBSN '11.

[16]  Ming-Wei Chang,et al.  Entity Linking on Microblogs with Spatial and Temporal Signals , 2014, TACL.

[17]  Jun Zhao,et al.  Collective entity linking in web text: a graph-based method , 2011, SIGIR.

[18]  Jian Su,et al.  Entity Linking with Effective Acronym Expansion, Instance Selection, and Topic Modeling , 2011, IJCAI.

[19]  Yue Gao,et al.  W2Go: a travel guidance system by automatic landmark ranking , 2010, ACM Multimedia.

[20]  I. King,et al.  Gaussian mixture distance for information retrieval , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[21]  Chenliang Li,et al.  Fine-grained location extraction from tweets with temporal awareness , 2014, SIGIR.

[22]  Tao Cheng,et al.  Entity Synonyms for Structured Web Search , 2012, IEEE Transactions on Knowledge and Data Engineering.

[23]  Gerhard Weikum,et al.  Gathering and ranking photos of named entities with high precision, high recall, and diversity , 2010, WSDM '10.

[24]  Christian Bizer,et al.  Multipedia: enriching DBpedia with multimedia information , 2011, K-CAP '11.

[25]  Jonathon S. Hare,et al.  Semantically Tagging Images of Landmarks , 2012, KECSM@ISWC.