Commenced in January 2007
Paper Count: 31515
Evolving Knowledge Extraction from Online Resources
Abstract:In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130979Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 673
 J. Piskorski and R. Yangarber, Information Extraction: Past, Present and Future. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 23–49.
 O. Etzioni, M. Cafarella, D. Downey, A.-M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates, “Unsupervised named-entity extraction from the web: An experimental study,” Artificial intelligence, vol. 165, no. 1, pp. 91–134, 2005.
 O. Etzioni, M. Banko, S. Soderland, and D. S. Weld, “Open information extraction from the web,” Communications of the ACM, vol. 51, no. 12, pp. 68–74, 2008.
 S. S. Tan, T. Y. Lim, L.-K. Soon, and E. K. Tang, “Learning to extract domain-specific relations from complex sentences,” Expert Systems with Applications, vol. 60, pp. 107 – 117, 2016.
 L. Shou, Z. Wang, K. Chen, and G. Chen, “Sumblr: Continuous summarization of evolving tweet streams,” in Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, ser. SIGIR ’13. New York, NY, USA: ACM, 2013, pp. 533–542.
 S. P. Kasiviswanathan, P. Melville, A. Banerjee, and V. Sindhwani, “Emerging topic detection using dictionary learning,” in Proceedings of the 20th ACM International Conference on Information and Knowledge Management, ser. CIKM ’11. New York, NY, USA: ACM, 2011, pp. 745–754.
 Z. Jiang, X. Liu, and L. Gao, “Dynamic topic/citation influence modeling for chronological citation recommendation,” in Proceedings of the 5th International Workshop on Web-scale Knowledge Representation Retrieval & Reasoning, ser. Web-KR ’14. New York, NY, USA: ACM, 2014, pp. 15–18.
 Z. Jiang, “Chronological scientific information recommendation via supervised dynamic topic modeling,” in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, ser. WSDM ’15. New York, NY, USA: ACM, 2015, pp. 453–458.
 Z. Jiang, X. Liu, and L. Gao, “Chronological citation recommendation with information-need shifting,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ser. CIKM ’15. New York, NY, USA: ACM, 2015, pp. 1291–1300.
 S. Teller, Data Visualization with D3.Js. Packt Publishing, 2013.
 L. Tan, “Pywsd: Python implementations of word sense disambiguation (wsd) technologies (software),” https://github.com/alvations/pywsd.