Abstract
In contrast with the amount of explosively increasing information on the Web, mobile users are suffering from low hardware capacity, poor interface, and high communication cost of their wireless devices. In this paper, we propose a framework for information summarization on wireless network. More importantly, we have focused on the template generation based on ontology. This system, thereby, can extract and send particular pieces of information relevant to the corresponding users, instead of sending the full texts themselves. Templates can be generated by not only user’s manual input but also semantic tagging, which is a process categorizing keywords into the most relevant concepts. Hence, in order to highlight a specific part of documents, these semantic templates can be applied as a set of rules. For conducting experiments, we have designed wireless reverse auction system in which participants can instantly send and receive the bidding messages through their mobile devices.
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References
Sadeh, N.: M-Commerce: Technologies, Services, and Business Models. Wiley Computer Publishing, Chichester (2002)
Freitag, D.: Using grammatical inference to improve precision in information extraction. In: ICML-1997 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition (1997)
Freitag, D., McCallum, A.: Information extraction using HMMs and shrinkage. In: Proceedings of the AAAI-1999 Workshop on Machine Learning for Information Extraction (1999)
Doorenbos, R.B., Etzioni, O., Weld, D.S.: A Scalable Comparison-Shopping Agent for the World Wide Web. In: Proceedings of Autonomous Agent 1997 (1997)
Sumita, K., Miike, S., Chino, T.: Automatic Abstract Generation Based on Document Structure Analysis and Its Evaluation as a Document Retreival Presentation Function. Systems and Computers 26(13), 32–43 (1995)
Wee, L.K.A., Tong, L.C., Tan, C.L.: A generic information extraction architecture for financial applications. Expert Systems with Applications 16(4), 343–356 (1999)
Kushmerick, N., Weld, D.S., Doorenbos, R.: Wrapper Induction for Information Extraction. In: Intl. Joint Conference on Artificial Intelligence, pp. 729–737 (1997)
Crocker, D.H.: Standard For The Format Of ARPA Internet Text Messages (1982), ftp://ftp.rfc-editor.org/in-notes/rfc822.txt
Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishers, Dordrecht (2002)
Soderland, S., Fisher, D., Aseltine, J., Lehnert, W.: CRYSTAL: Inducing a Conceptual Dictionary. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1314–1319 (1995)
Ciravegna, F., Petrelli, D.: User involvement in customizing adaptive Information Extraction. In: Proceedings of the IJCAI-2001 Workshop on Adaptive Text Extraction and Mining (2001)
Open Mobile Alliance Ltd (2002), http://www.openmobilealliance.org
WAP 2.0 Specifications (2002), http://www.wapforum.org
Wireless Short Message Service, SMS (2002), http://www.iec.org
Auction Korea, http://www.auction.co.kr
Nokia Mobile Internet Toolkit 4.1, http://www.forum.nokia.com/main
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© 2005 Springer-Verlag Berlin Heidelberg
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Jung, J.J., Park, SB., Jo, GS. (2005). Semantic Template Generation Based Information Summarization for Mobile Devices. In: Shimojo, S., Ichii, S., Ling, TW., Song, KH. (eds) Web and Communication Technologies and Internet-Related Social Issues - HSI 2005. HSI 2005. Lecture Notes in Computer Science, vol 3597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527725_15
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DOI: https://doi.org/10.1007/11527725_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27830-6
Online ISBN: 978-3-540-31808-8
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