This paper presents a generic model for Semantic Enrichment of Location Context where the main attributes of places are described by tags. These tags are automatically extracted by applying natural language processing and information extraction techniques that have been thoroughly applied and tested using the World Wide Web as the primary source. Here, we are particularly focused on extracting information that allows an external system to distinguish one place from other places that are spatially or conceptually close. This is because the meaning of a place is a function of its most salient features, present in the textual descriptions found in online resources about that place. In the situation under investigation, places correspond to Points-of-Interest (POIs), as these are abundant on the Web. The applicability of such model is demonstrated through its implementation over real collected POIs. As an illustrative output of the system, a set of examples is also presented.
Keywords
context, information extraction, location in context-awareness, representation, semantic processing
Related Project
Crowds - Understanding urban land use from digital footprints of crowds
Conference
1st International Workshop on Context Discovery and Data Mining part of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , August 2012
DOI
Cited by
Year 2017 : 1 citations
Kiseleva, J., & de Rijke, M. (2017). Evaluating Personal Assistants on Mobile devices. arXiv preprint arXiv:1706.04524.
Year 2016 : 2 citations
Reichenbacher, Tumasch, et al. "Assessing geographic relevance for mobile search: A computational model and its validation via crowdsourcing." Journal of the Association for Information Science and Technology 67.11 (2016): 2620-2634.
Kiseleva, Julia, Jaap Kamps, and Charles LA Clarke. "Contextual search and exploration." Information Retrieval. Springer International Publishing, 2016. 3-23.
Year 2015 : 3 citations
Kiseleva, J. (2015, August). Using contextual information to understand searching and browsing behavior. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1059-1059). ACM.
Julia Kiseleva, Jaap Kamps, and Charles L. A. Clarke. Contextual Search and Exploration. RuSSIR Young Scientist Conference. August 2015. https://www.researchgate.net/profile/Julia_Kiseleva2/publication/283356889_Contextual_Search_and_Exploration/links/56376c4408aeb786b7044c95.pdf
JOTHIS CHEMBATH, Dr.S.K.MAHENDRAN. AN INSIGHT IN TO WEB MINING IN PARTICULAR WEBLOG FILES TO UNDERSTAND AND PREDICT BEHAVIORAL PATTERNS OF WEB USERS USING INTEGRATED MARKOV MODEL. International Journal of Commerce, Management and Computer Application. Volume(1) - Issue(4), 2015
pp 7 - 10, DOI:123.10.48
Year 2013 : 1 citations
Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy, and Toon Calders. 2013. Predicting Current User Intent with Contextual Markov Models. In Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW '13). IEEE Computer Society, Washington, DC, USA, 391-398. DOI=10.1109/ICDMW.2013.143 http://dx.doi.org/10.1109/ICDMW.2013.143
