Efficient multi-event monitoring using built-in search engines

Zhaoman ZHONG, Zongtian LIU, Yun HU, Cunhua LI

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PDF(448 KB)
Front. Comput. Sci. ›› 2016, Vol. 10 ›› Issue (2) : 281-291. DOI: 10.1007/s11704-015-4432-3
RESEARCH ARTICLE

Efficient multi-event monitoring using built-in search engines

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Abstract

Users of the internet often wish to follow certain news events, and the interests of these users often overlap. General search engines (GSEs) cannot be used to achieve this task due to incomplete coverage and lack of freshness. Instead, a broker is used to regularly query the built-in search engines (BSEs) of news and social media sites. Each user defines an event profile consisting of a set of query rules called event rules (ERs). To ensure that queries match the semantics of BSEs, ERs are transformed into a disjunctive normal form, and separated into conjunctive clauses (atomic event rules, AERs). It is slow to process all AERs on BSEs, and can violate query submission rate limits. Accordingly, the set of AERs is reduced to eliminate AERs that are duplicates, or logically contained by other AERs. Five types of event are selected for experimental comparison and analysis, including natural disasters, accident disasters, public health events, social security events, and negative events of public servants. Using 12 BSEs, 85 ERs for five types of events are defined by five users. Experimental comparison is conducted on three aspects: event rule reduction ratio, number of collected events, and that of related events. Experimental results in this paper show that event rule reduction effectively enhances the efficiency of crawling.

Keywords

information retrieval / event retrieval / event monitoring / BSEs / event rule reduction

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Zhaoman ZHONG, Zongtian LIU, Yun HU, Cunhua LI. Efficient multi-event monitoring using built-in search engines. Front. Comput. Sci., 2016, 10(2): 281‒291 https://doi.org/10.1007/s11704-015-4432-3

References

[1]
Lawrence S, Giles CL. Accessibility of information on the Web. Nature, 1999, 107–109
CrossRef Google scholar
[2]
Selberg E, Etzioni O. The MetaCrawler architecture for resource aggregation on the Web. IEEE Expert, 1997, 12(1): 11–14
CrossRef Google scholar
[3]
Fellbaun C, Miller G A. WordNet: A lexical database for the English language [EB/OL]. 2006
[4]
Li W J, Mu M L, Lu Q, Wei X, Yuan C F. Extractive summarization using inter-and intra-event relevance. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL. 2006, 369–376
CrossRef Google scholar
[5]
Filatova E, Hatzivassiloglou V. Domain-independent detection, extraction, and labeling of atomic events. In: Proceedings of the 2003 Recent Advances in Natural Language Processing. 2003, 145–152
[6]
Zhong Z M, Liu Z T, Li C H, Guan Y. Event ontology reasoning based on event class influence factors. International Journal ofMachine Learning and Cybernetics, 2012, 3(2): 133–139
[7]
Demers A J, Gehrke J, Panda B, Riedewald M, Sharma V, White W. Cayuga: A general purpose event monitoring system. In: Proceeding of Biennial Conference on Innovative Data Systems Research. 2007, 412–422
[8]
Li C H, Hu Y, Zhong Z M. An event ontology construction approach to web crime mining. In: Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery. 2010, 2441–2445
CrossRef Google scholar
[9]
Albakour M D, Macdonald C, Ounis L. Identifying local events by using microblogs as social sensors. In: Proceedings of the 10th International Conference on Open Research Areas in Information Retrieval. 2013, 173–180
[10]
Lee S J, Lee S, Kim K, Park J. Bursty event detection from text streams for disaster management. In: Proceedings of the International Conference Companion on World Wide Web. 2012, 679–681
CrossRef Google scholar
[11]
Zhao W X, Chen R H, Fan K, Yan H F, Li X M. A novel burst-based text representation model for scalable event detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. 2012, 43–47
[12]
Zhang L M, Jia Y, Zhou B, Zhao J H, Hong F. Online bursty events detection based on emotions. Chinese Journal of Computers, 2013, 1659–1667
[13]
Chakrabarti S, Den Berg M V, Dom B. Focused crawling: A new approach to topic-specific web resource discovery. Computer Networks, 1999, 1623–1640
CrossRef Google scholar
[14]
Medelyan O, Schulz S, Paetzold J, Poprat M, Markó K. Language specific and topic focused web crawling. In: Proceedings of the Language Resources Conference LREC. 2006, 267–269
[15]
Sotiris B, Euripides G M, Petrakis E M. Improving the performance of focused web crawlers. Data & Knowledge Engineering, 2009, 68(10): 1001–1013
CrossRef Google scholar
[16]
Lee Y H, Na S H, Lee J H. Utilizing local evidence for blog feed search. Information Retrieval, 2012, 15(2): 157–177
CrossRef Google scholar
[17]
Du Y J, Pen Q Q, Gao Z Q. A topic-specific crawling strategy based on semantics similarity. Data & Knowledge Engineering, 2013, 88: 75–93
CrossRef Google scholar
[18]
Jiang J T, Song X Y, Yu N H, Lin C Y. Focus: Learning to crawl web forums. IEEE Transactions on Knowledge and Data Engineering, 2013, 1293–1306
CrossRef Google scholar
[19]
Liu L, Peng T. Clustering-based topical web crawling using CFu-tree guided by link-context. Frontiers of Computer Science, 2014, 8(4): 581–595
CrossRef Google scholar
[20]
Metzler D, Cai C X, Hovy E. Structured event retrieval over microblog archives. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2012, 646–655
[21]
Steven S, Martine D C, Etienne E K. Reasoning about fuzzy temporal information from the web: towards retrieval of historical events. Soft Computing, 2010, 14(8): 869–886
CrossRef Google scholar
[22]
Zhong Z M, Zhu P, Li C H, Guan Y, Liu Z T. Research on eventoriented query expansion based on local analysis. Journal of the China Society for Scientific and Technical Information, 2012, 31(2): 151–159
[23]
Zhong ZM, Li C H, Liu Z T, Dai HW.Web news oriented event multielements retrieval. Journal of Software, 2013, 2366–2378
[24]
Wu P B, Chen Q X, Ma L. Study on intelligent retrieval of event relevant documents based on event frame. Journal of Chinese Information Processing, 2003, 17(6): 25–30
[25]
Fu T J, Abbasi A, Chen H C. A focused crawler for dark Web forums. Journal of the American Society for Information Science and Technology, 2010, 61(6): 1213–1231
CrossRef Google scholar
[26]
Yang L Y, Li H J, Zhang Y K. The research on classification system of accidental news corpus. In: Proceedings of the 25th Conference on Frontier and Progress of Chinese Information Processing. 2006, 403–409
[27]
Menczer F, Pant G, Srinivasan P. Topical web crawlers: evaluating adaptive algorithms. ACM Transactions on Internet Technology, 2004, 4(4): 378–419
CrossRef Google scholar
[28]
Martinez-Romo J, Araujo L. Updating broken Web links: An automatic recommendation system. Information Processing and Management, 2012, 48(2): 183–203
CrossRef Google scholar
[29]
Melanie N, Markus N, Rudolf M, Bianka T. Focused crawling for buildingWeb comment corpora. In: Proceedings of the 10th IEEE Consumer Communications and Networking Conference. 2013, 685–688

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