Abstract
Most research work on news mining nowadays covers phrase and topic level. A few works conducted on logical level mainly focus on personalized news service and no special efforts are put on the applications of ontology techniques on deep news mining. In this paper, we demonstrate a whole strategy for deeply understanding event-focused news taking the advantage of ontology representation and ontology reasoning. We propose an ontology-enriched news deep understanding framework ONDU which addresses the following problems: (1) how to transfer parsed news content into logical triples by using domain ontology. (2) The application of ONDU based on the reasoning results from the ontology reasoner TrOWL over the RDF data expressing the news. Through this whole strategy we can detect the inconsistence among multiple news articles and compare the different information implied in different news. We can even integrate a set of news content through merging the RDF data. The empirical experiment conducted on news from several portals shows the effectiveness and usefulness of our method.
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References
Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B.: Scene: a scalable two-stage personalized news recommendation system. In: SIGIR 2011, pp. 125–134 (2011)
Wayne, C.L.: Multiligual topic detection and tracking: successful research enabled by corpora and evaluation. In: Conference: Language Resources and Evaluation - LREC (2000)
Conlan, O., O’Keeffe, I., Tallon, S.: Combining Adaptive Hypermedia Techniques and Ontology Reasoning to Produce Dynamic Personalized News Services. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 81–90. Springer, Heidelberg (2006)
Leskovec, J., Backstrom, L., Kleinberg, J.M.: Meme-tracking and the dynamics of the news cycle. In: SIGKDD 2009, pp. 457–466 (2009)
Kanhabua, N., Blanco, R., Matthews, M.: Ranking related news prediction. In: SIGIR 2011, pp. 755–764 (2011)
Pouliquen, B., Steinberger, R., Deguernel, O.: Story tracking: linking similar news over time and across languages. In: Colling 2008 MMIES Workshop, pp. 49–56 (2008)
Shan, D., Zhao, W.X., Chen, R., Shu, B., Wang, Z., Yao, J., Yan, H., Li, X.: Eventsearch: a system for event discovery and retrieval on multi-type historical data. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1564–1567 (2012)
Leskovec, J., Grobelnik, M., Milic-Franling, N.: Learning sub-structures of document semantic graphs for document summarization. In: Proceedings of the 7th International Multi-Conference Information Society (2004)
Kalfoglou, Y., Kalfoglou, Y., Domingue, J., Domingue, J., Motta, E., Motta, E., Vargas-Vera, M., Vargas-vera, M., Shum, S.B., Shum, S.B.: myPlanet: an ontology-driven Web-based personalized news service. In: Proceedings of the IJCAI 2001 Workshop on Ontologies and Information Sharing (2001)
Hou, L., Li, J.Z., Tang, J., Liu, Y.K., Zheng, Q.: Newsminer: multifaceted news analysis for event search. To be Appeared in ACM Transactions on Information System 9(4) (2012)
Brank, J., Grobelnik, M., Mladenić, D.: A survey of ontology evaluation techniques. In: Proceedings of the Conference on Data Mining and Data Warehouses, SiKDD 2005 (2005)
Subhashini, R., Akilandeswari, J.: A survey on ontology construction methodologies. International Journal of Enterprise Computing and Business Systems 1(1), 60–72 (2011)
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Wen, X., Ma, X., Li, J., Pan, J.Z., Xie, J. (2013). Toward Ontology Representation and Reasoning for News. In: Qi, G., Tang, J., Du, J., Pan, J.Z., Yu, Y. (eds) Linked Data and Knowledge Graph. CSWS 2013. Communications in Computer and Information Science, vol 406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54025-7_16
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DOI: https://doi.org/10.1007/978-3-642-54025-7_16
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