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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/

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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

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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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