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Multi-document semantic relation extraction for news analytics

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Abstract

Given the overwhelming amounts of information in our current 24/7 stream of new incoming articles, new techniques are needed to enable users to focus on just the key entities and concepts along with their relationships. Examples include news articles but also business reports and social media. The fact that relevant information may be distributed across diverse sources makes it particularly challenging to identify relevant connections. In this paper, we propose a system called MuReX to aid users in quickly discerning salient connections and facts from a set of related documents and viewing the resulting information as a graph-based visualization. Our approach involves open information extraction, followed by a careful transformation and filtering approach. We rely on integer linear programming to ensure that we retain only the most confident and compatible facts with regard to a user query, and finally apply a graph ranking approach to obtain a coherent graph that represents meaningful and salient relationships, which users may explore visually. Experimental results corroborate the effectiveness of our proposed approaches, and the local system we developed has been running for more than one year.

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Notes

  1. https://safetyapp.shinyapps.io/GoWvis/

  2. http://maggie.lt.informatik.tu-darmstadt.de/thesis/master/NetworksOfNames

  3. http://tagesnetzwerk.de

  4. http://www.newsleak.io/

  5. Given a relational triple extracted by ClausIE, OLLIE, or Open IE 4, only when its confidence is greater than 0.85 is it judged as being a suitable extraction.

  6. https://github.com/pilehvar/ADW

  7. https://duc.nist.gov/

  8. http://research.signalmedia.co/newsir16/signal-dataset.html

  9. https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/clausie/

  10. http://knowitall.github.io/ollie/

  11. https://nlp.stanford.edu/software/openie.shtml

  12. https://github.com/knowitall/openie

  13. https://github.com/uma-pi1/minie

  14. An entity or concept is regarded as a topic concept if it occurs in the topic words list as described in Section 4.1.

  15. For popular OpenIE systems such as ClausIE, OLLIE, and Open IE 4, we rely on the confidence value computed by each system itself as the confidence score of each of facts.

  16. http://tomcat.apache.org/

  17. http://www.mysql.com/

  18. http://avalonjs.coding.me/

  19. http://jquery.com/

References

  1. Angeli, G., Premkumar, M. J. J., Manning, C. D.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 344–354 (2015)

  2. Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, vol. 7, pp. 2670–2676 (2007)

  3. Benikova, D., Fahrer, U., Gabriel, A., Kaufmann, M., Yimam, S.M., von Landesberger, T., Biemann, C.: Network of the day: Aggregating and visualizing entity networks from online sources

  4. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)

  5. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E. R., Mitchell, T. M.: Toward an architecture for never-ending language learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (2010)

  6. Council, I.: EventsML-G2: A data model and format for collecting and distributing event information (2014). http://www.iptc.org/site/News_Exchang_Formats/EventsML-G2

  7. Council, I.P.T.: rnews (2014). http://dev.iptc.org/rNews

  8. Council, I.P.T.: NewsML-G2 2.28 specification (2019). https://iptc.org/std/NewsML-G2/2.28/specification/NewsML-G2-2.28-specification.html

  9. Del Corro, L., Gemulla, R.: ClausIE: clause-based open information extraction. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 355–366. ACM (2013)

  10. Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1535–1545. Association for Computational Linguistics (2011)

  11. Falke, T., Gurevych, I.: GraphDocExplore: A framework for the experimental comparison of graph-based document exploration techniques. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 19–24 (2017)

  12. Fuchs, C. A., Peres, A.: Quantum-state disturbance versus information gain: Uncertainty relations for quantum information. Phys. Rev. A 53(4), 2038 (1996)

    Article  Google Scholar 

  13. Galárraga, L., Heitz, G., Murphy, K., Suchanek, F. M.: Canonicalizing open knowledge bases. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM ’14, pp 1679–1688. ACM, New York, NY, USA (2014), 10.1145/2661829.2662073

  14. Gashteovski, K., Gemulla, R., Del Corro, L.: MinIE: minimizing facts in open information extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2630–2640 (2017)

  15. Ge, T., Wang, Y., de Melo, G., Li, H., Chen, B.: Visualizing and curating knowledge graphs over time and space. pp. 25–30 (2016). https://www.aclweb.org/anthology/P16-4005.pdf

  16. Google Microsoft, Y.: Schemas – schema.org. (2012). http://www.schema.org/docs/schemas.html

  17. Hearst, M. A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Annual Meeting of the Association for Computational Linguistics, pp. 539–545. Association for Computational Linguistics (1992)

  18. Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutierrez, C., Labra Gayo, J.E., Kirrane, S., Neumaier, S., Polleres, A., Navigli, R., Ngonga Ngomo, A.C., Rashid, S.M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., Zimmermann, A.: Knowledge graphs. arXiv:https://arxiv.org/abs/2003.02320 (2020)

  19. Hou, L., Li, J., Wang, Z., Tang, J., Zhang, P., Yang, R., Zheng, Q.: Newsminer: Multifaceted news analysis for event search. Knowl.-Based Syst. 76, 17–29 (2015)

    Article  Google Scholar 

  20. Hu, G., Qin, Y., Shao, J.: Personalized travel route recommendation from multi-source social media data Multimedia Tools and Applications (2018)

  21. Ji, H., Favre, B., Lin, W. P., Gillick, D., Hakkani-Tur, D., Grishman, R.: Open-Domain Multi-Document Summarization via Information Extraction: Challenges and Prospects Multi-Source, Multilingual Information Extraction and Summarization, Pp. 177–201. Springer (2013)

  22. Kochtchi, A., Landesberger, T.v., Biemann, C.: Networks of Names: Visual Exploration and Semi-Automatic Tagging of Social Networks from Newspaper Articles. In: Computer Graphics Forum, Vol. 33, pp. 211–220. Wiley Online Library (2014)

  23. Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 497–506. ACM (2009)

  24. Li, J., Li, J., Tang, J.: A flexible topic-driven framework for news exploration. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2007 (2007)

  25. Lin, C. X., Zhao, B., Mei, Q., Han, J.: PET: A statistical model for popular events tracking in social communities. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 929–938. ACM (2010)

  26. Mann, G.: Multi-document relationship fusion via constraints on probabilistic databases. In: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference, pp. 332–339 (2007)

  27. Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., McClosky, D.: The Stanford CoreNLP Natural Language Processing Toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 55–60 (2014)

  28. Mausam, M.: Open information extraction systems and downstream applications. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 4074–4077. AAAI Press (2016)

  29. Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 198–207. ACM (2005)

  30. Mihalcea, R., Tarau, P.: TextRank: Bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (2004)

  31. Miller, G. A.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  32. Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Yang, B., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., et al.: Never-ending learning. Communications of the ACM 61(5), 103–115 (2018)

    Article  Google Scholar 

  33. Pilehvar, M. T., Jurgens, D., Navigli, R.: Align, disambiguate and walk: a unified approach for measuring semantic similarity. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1341–1351 (2013)

  34. Pouliquen, B., Steinberger, R., Deguernel, O.: Story tracking: linking similar news over time and across languages. In: Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization, pp. 49–56. Association for Computational Linguistics (2008)

  35. Rouces, J., de Melo, G., Hose, K.: Heuristics for connecting heterogeneous knowledge via FrameBase. In: Proceedings of ESWC 2016, Lecture Notes in Computer Science, pp. 20–35. Springer (2016). https://link.springer.com/chapter/10.1007/978-3-319-34129-3_2

  36. Schmitz, M., Bart, R., Soderland, S., Etzioni, O., et al.: Open language learning for information extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 523–534. ACL (2012)

  37. Shahaf, D., Guestrin, C.: Connecting the dots between news articles. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 623–632. ACM (2010)

  38. 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. ACM (2012)

  39. Sheng, Y., Xu, Z., Wang, Y., Zhang, X., Jia, J., You, Z., de Melo, G.: Visualizing multi-document semantics via open domain information extraction. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 695–699. Springer (2018)

  40. Spitkovsky, V. I., Chang, A. X.: A cross-lingual dictionary for English Wikipedia concepts. In: Proceedings of the 8th International Conference on Language Resources and Evaluation, pp. 3168–3175 (2012)

  41. Sridhar, V. K. R.: Unsupervised topic modeling for short texts using distributed representations of words. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp. 192–200 (2015)

  42. Suchanek, F. M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)

  43. Tandon, N., de Melo, G.: Information extraction from web-scale n-gram data. In: Zhai, C., Yarowsky, D. , Viegas, E. , Wang, K. , Vogel, S. (eds.) Web N-gram Workshop. Workshop of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, vol. 5803, pp. 8–15. ACM (2010). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.365.2318

  44. Tandon, N., de Melo, G., De, A., Weikum, G.: Knowlywood: Mining activity knowledge from Hollywood narratives. In: Proceedings of CIKM 2015, pp. 223–232. ACM. (2015). https://dl.acm.org/doi/10.1145/2806416.2806583

  45. Tandon, N., de Melo, G., Suchanek, F. M., Weikum, G.: WebChild: Harvesting and organizing commonsense knowledge from the web. In: Carterettem, B., Diaz, F., Castillo, C., Metzler, D. (eds.) Proceedings of ACM WSDM 2014, pp. 523–532. ACM (2014)

  46. Tandon, N., de Melo, G., Weikum, G.: Acquiring comparative commonsense knowledge from the web. In: Proceedings of AAAI 2014, pp. 166–172. AAAI. (2014). https://dl.acm.org/doi/10.5555/2893873.2893902

  47. Tixier, A., Skianis, K., Vazirgiannis, M.: GoWvis: a web application for graph-of-words-based text visualization and summarization (2016)

  48. Wang, L., Guo, Z., Wang, Y., Cui, Z., Liu, S., de Melo, G.: Social media vs. news media: Analyzing real-world events from different perspectives. In: Proceedings of DEXA 2018, LNCS, vol. 11030, pp. 471–479. Springer Verlag (2018), https://doi.org/10.1007/978-3-319-98812-243. https://link.springer.com/chapter/10.1007/978-3-319-98812-243

  49. Xu, T., Liu, D., Chen, E., Cao, H., Tian, J.: Towards Annotating Media Contents through Social Diffusion Analysis. In: 2012 IEEE 12Th International Conference on Data Mining, pp. 1158–1163. IEEE (2012)

  50. Xu, T., Zhu, H., Chen, E., Huai, B., Xiong, H., Tian, J.: Learning to annotate via social interaction analytics. Knowledge and information systems 41(2), 251–276 (2014)

    Article  Google Scholar 

  51. Yang, Q., Cheng, Y., Wang, S., de Melo, G.: HiText: Text reading with dynamic salience marking. In: Proceedings of WWW 2017, pp. 311–319. ACM (2017). https://dl.acm.org/citation.cfm?id=3041021.3054168

  52. Yimam, S. M., Ulrich, H., von Landesberger, T., Rosenbach, M., Regneri, M., Panchenko, A., Lehmann, F., Fahrer, U., Biemann, C., Ballweg, K.: new/s/leak–information extraction and visualization for investigative data journalists. In: Proceedings of ACL 2016 (System Demonstrations). https://doi.org/10.18653/v1/P16-4028, https://www.aclweb.org/anthology/P16-4028/, pp 163–168. Association for Computational Linguistics (2016)

  53. Yu, D., Huang, L., Ji, H.: Open relation extraction and grounding. In: Proceedings of the 8th International Joint Conference on Natural Language Processing, pp. 854–864 (2017)

  54. Zhu, C., Zhu, H., Ge, Y., Chen, E., Liu, Q., Xu, T., Xiong, H.: Tracking the evolution of social emotions with topic models. Knowl. Inf. Syst. 47(3), 517–544 (2016)

    Article  Google Scholar 

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Acknowledgments

This paper was partially supported by National Natural Science Foundation of China (Nos. 61572111 and 61876034). Yafang Wang’s research was supported by the National Natural Science Foundation of China (No. 61503217).

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Authors and Affiliations

  1. SMILE Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yongpan Sheng & Zenglin Xu

  2. Ant Financial Services Co., Hangzhou, Zhejiang, China

    Yafang Wang

  3. Department of Computer Science, Rutgers University–New Brunswick, Piscataway, NJ, USA

    Gerard de Melo

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  1. Yongpan Sheng
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  2. Zenglin Xu
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  4. Gerard de Melo
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Correspondence to Gerard de Melo.

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This article belongs to the Topical Collection: Special Issue on Web and Big Data 2019

Guest Editors: Jie Shao, Man Lung Yiu, and Toyoda Masashi

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Sheng, Y., Xu, Z., Wang, Y. et al. Multi-document semantic relation extraction for news analytics. World Wide Web 23, 2043–2077 (2020). https://doi.org/10.1007/s11280-020-00790-2

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