Abstract
In this paper we present an approach, inspired by honey bees, that allows us to take a glance at current events by exploring a portion of the Web and extracting keywords, relevant to current news stories. Not unlike the bees, that cooperate together to retrieve little bits of food, our approach uses agents to select random keywords and carry them from one article to another, landing only on the articles relevant to the keyword. Keywords that best represent multiple articles are selected, while keywords not relevant to articles are subsequently discarded and not explored further. Our results show, that with this approach, it is possible to extract keywords relevant to news stories, without utilizing learning methods, or analysis of a data corpus.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bohne, T., Rönnau, S., Borghoff, U.M.: Efficient keyword extraction for meaningful document perception. In: Proceedings of the 11th ACM Symposium on Document Engineering, DocEng 2011, pp. 185–194. ACM, New York (2011)
Karaboga, D.: An idea based on Honey Bee Swarm for Numerical Optimization. Tech. Rep. TR06, Erciyes University (October 2005)
Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31, 61–85 (2009)
Kriz, J.: Keyword extraction based on implicit feedback. Information Sciences and Technologies Bulletin of the ACM Slovakia 4, 43–46 (2012)
Kuzar, T.: Clustering on social web. Information Sciences and Technologies Bulletin of the ACM Slovakia 5, 34–42 (2013)
Lee, S., Kim, H.J.: News keyword extraction for topic tracking. In: Fourth International Conference on Networked Computing and Advanced Information Management, NCM 2008, vol. 2, pp. 554–559 (September 2008)
Lemmens, N., et al.: Bee Behaviour in Multi-agent Systems (A Bee Foraging Algorithm). In: Adaptive Agents and MAS III, Berlin, pp. 145–156 (2008)
Leuski, A.: Evaluating document clustering for interactive information retrieval. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 33–40. ACM, New York (2001)
Lorenzi, F., Santos, D.S., Bazzan, A.L.C.: Negotiation for task allocation among agents in case-base recommender systems: a swarm-intelligence approach. In: Proceedings of the Workshop Multi-Agent Information Retrieval and Recommender Systems - Nineteenth International Conference on Artificial Intelligence (IJCAI 2005), pp. 23–27 (2005)
Memari, A., Amer, M., Marx Gmez, J.: A beehive-like multi-agent solution to enhance findability of semantic web services and facilitate personalization within a p2p network. In: ICT Innovations, pp. 227–236. Springer (2010)
Navrat, P., Jelinek, T., Jastrzembska, L.: Bee hive at work: A problem solving, optimizing mechanism. In: World Congress on Nature Biologically Inspired Computing, NaBIC 2009, pp. 122–127 (December 2009)
Navrat, P.: Bee hive metaphor for web search. Communication and Cognition-Artificial Intelligence 23(1-4), 15–20 (2006)
Navrat, P., Jastrzembska, L., Jelinek, T., Ezzeddine, A.B., Rozinajova, V.: Exploring social behaviour of honey bees searching on the web. In: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, pp. 21–25. IEEE Computer Society, Washington, DC (2007)
Navrat, P., Kovacik, M.: Web search engine as a bee hive. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2006, pp. 694–701. IEEE Computer Society, Washington, DC (2006)
Rambharose, T., Nikov, A.: Computational intelligence-based personalization of interactive web systems. WSEAS Trans. Info. Sci. and App. 7, 484–497 (2010)
Schultze, S.J.: A collaborative foraging approach to web browsing enrichment. In: CHI 2002 Extended Abstracts on Human Factors in Computing Systems, CHI 2002, pp. 860–861. ACM, New York (2002)
Teodorović, D.: Bee colony optimisation (bco). In: Lim, C.P., Jain, L.C., Dehuri, S. (eds.) Innovations in Swarm Intelligence. SCI, vol. 248, pp. 39–60. Springer, Heidelberg (2009)
Toda, H., Kataoka, R.: A clustering method for news articles retrieval system. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, pp. 988–989. ACM, New York (2005)
Toda, H., Kitagawa, H., Fujimura, K., Kataoka, R.: Topic structure mining using temporal co-occurrence. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, New York, NY, USA, pp. 236–241 (2008)
Turney, P.D.: Learning algorithms for keyphrase extraction. Information Retrieval 2, 303–336 (2000)
Vadrevu, S., Teo, C.H., Rajan, S., Punera, K., Dom, B., Smola, A.J., Chang, Y., Zheng, Z.: Scalable clustering of news search results. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 675–684. ACM, New York (2011)
Wang, Y., Kitsuregawa, M.: Link based clustering of web search results. In: Wang, X.S., Yu, G., Lu, H. (eds.) WAIM 2001. LNCS, vol. 2118, pp. 225–236. Springer, Heidelberg (2001)
Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: Kea: practical automatic keyphrase extraction. In: Proceedings of the Fourth ACM Conference on Digital Libraries, pp. 254–255. ACM, New York (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Návrat, P., Sabo, Š. (2013). Beehive Based Machine to Give Snapshot of the Ongoing Stories on the Web. In: Gavrilova, M.L., Tan, C.J.K., Abraham, A. (eds) Transactions on Computational Science XXI. Lecture Notes in Computer Science, vol 8160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45318-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-45318-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45317-5
Online ISBN: 978-3-642-45318-2
eBook Packages: Computer ScienceComputer Science (R0)