Skip to main content
SpringerLink
Log in

An Information Retrieval System for Decision Support: An Arctic-Related Mass Media Case Study

  • Published:
Scientific and Technical Information Processing Aims and scope

Abstract

This paper discusses the problem of building a comprehensive information retrieval system that facilitates the decision-making process in a specified wide topic. We analyze the requirements for such a system, types of information sources, and typical search queries and propose an architecture and an integrated pipeline. We also present a case study in the field of Arctic exploration (oil & mining, ecology issues, etc.). The results are also presented, including vibrant topics and typical associations between entities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Imran, M., et al., Processing social media messages in mass emergency: A survey, ACM Comput. Surv., 2015, vol. 47, no. 4, p. 67.

    Article  MathSciNet  Google Scholar 

  2. Petrovic, S., Real-Time Event Detection in Massive Streams, 2013.

    Google Scholar 

  3. Li, R., et al., Tedas: A twitter-based event detection and analysis system, 2012 IEEE 28th International Conference on Data Engineering (ICDE), 2012, pp. 1273–1276.

    Chapter  Google Scholar 

  4. Li Zheng, Chao Shen, Liang Tang, et al., Disaster SitRep–A vertical search engine and information analysis tool in disaster management domain, Proceedings of 2012 IEEE 13th International Conference on Information Reuse and Integration (IRI), 2012, pp. 457–465.

    Google Scholar 

  5. Ashktorab, Z., Brown, C., Nandi, M., and Culotta, A., Tweedr: Mining Twitter to inform disaster response, Proceedings of ISCRAM, 2014, pp. 354–358.

    Google Scholar 

  6. Xiaohua, L., Shaodian, Zh., Furu, W., and Ming, Zh., Recognizing named entities in tweets, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, 2011, pp. 359–367.

    Google Scholar 

  7. Bhattacharya, A., Tiwari, M.K., and Harding, J.A., A framework for ontology based decision support system for e-learning modules, business modeling and manufacturing systems, J. Intell. Manuf., 2012, vol. 23, no. 5, 1763–1781.

    Article  Google Scholar 

  8. Rao, L., Mansingh, G., and Osei-Bryson, K.M., Building ontology based knowledge maps to assist business process re-engineering, Decis. Support Syst., 2012, vol. 52, no. 3, pp. 577–589.

    Article  Google Scholar 

  9. Hersovici, M., et al., The shark-search algorithm. An application: Tailored web site mapping, Comput. Networks ISDN Syst., 1998, vol. 30, no. 1, pp. 317–326.

    Article  Google Scholar 

  10. Chen, Z., et al., An improved shark-search algorithm based on multi-information, IEEE Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, 2007, vol. 4, pp. 659–658.

    Article  Google Scholar 

  11. Su, C., et al., An efficient adaptive focused crawler based on ontology learning, IEEE Fifth International Conference on Hybrid Intelligent Systems, 2005. HIS'05, 2005, p. 6.

    Google Scholar 

  12. Liu, H., Janssen, J., and Milios, E., Using HMM to learn user browsing patterns for focused web crawling, Data Knowl. Eng., 2006, vol. 59, no. 2, pp. 270–291.

    Article  Google Scholar 

  13. Blanvillain, O., Kasioumis, N., and Banos, V., Blog-Forever Crawler: Techniques and algorithms to harvest modern weblogs, Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14), ACM, 2014, p. 7.

    Google Scholar 

  14. Florian, R., et al., Named entity recognition through classifier combination, Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, 2003, vol. 4, pp. 168–171.

    Article  Google Scholar 

  15. Al-Rfou, R., et al., Polyglot-NER: Massive multilingual named entity recognition, Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, 2015.

    Google Scholar 

  16. Wikipedia. http://wikipedia.org. Cited January 20, 2016.

  17. Bollacker, K., et al., Freebase: A collaboratively created graph database for structuring human knowledge, Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, 2008, pp. 1247–1250.

    Chapter  Google Scholar 

  18. Manning, C.D., et al., Introduction to Information Retrieval, Cambridge: Cambridge University Press, 2008, vol. 1, p. 496.

    MATH  Google Scholar 

  19. Sochenkov, I.V. and Suvorov, R.E., Services of full-text search in the information-analytical system (Part 1), Inf. Tekhnol. Vychisl. Sist., 2013, no. 2, pp. 69–78.

    Google Scholar 

  20. Takase, S., Okazaki, N., and Inui, K., Fast and Large-Scale Unsupervised Relation Extraction, 2015.

    Google Scholar 

  21. Angeli, G., Premkumar, M.J., and Manning, C.D., Leveraging linguistic structure for open domain information extraction, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL, 2015, pp. 26–31.

    Google Scholar 

  22. TAC Knowledge Base Population, NIST Information Technology Laboratory, 2015. http://www.nist.gov/tac/2015/KBP/. Cited January 20, 2016.

  23. Hoffmann, R., et al., Knowledge-based weak supervision for information extraction of overlapping relations, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011, vol. 1, pp. 541–550.

    Google Scholar 

  24. Scrapy. A Fast and Powerful Scraping and Web Crawling Framework. http://scrapy.org/. Cited January 20, 2016.

    Google Scholar 

  25. Osipov, G., et al., Relational-situational method for intelligent search and analysis of scientific publications, Proceedings of the Integrating IR Technologies for Professional Search Workshop, 2013, pp. 57–64.

    Google Scholar 

  26. Agrawal, R., Imielinski, T., and Swami, A., Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993, vol. 22, pp. 207–216.

    Article  Google Scholar 

  27. Blei, D.M., Probabilistic topic models, Commun. ACM, 2012, vol. 55, no. 4, pp. 77–84.

    Article  Google Scholar 

  28. Devyatkin, D.A., Suvorov, R.E., and Sochenkov, I.V., A method of thematic clustering of large-scale collections of scientific and technical documents, Inf. Tekhnol. Vychisl. Sist., 2013, no. 1, pp. 33–42.

    Google Scholar 

  29. Haklay, M. and Weber, P., Openstreetmap: User-generated street maps, Pervasive Comput., 2008, vol. 7, no. 4, pp. 12–18.

    Article  Google Scholar 

  30. Titan: Distributed Graph Database, DataStax, 2016. http://thinkaurelius.github.io/titan/. Cited January 20, 2016.

  31. Lakshman, A. and Malik, P., Cassandra: A decentralized structured storage system, ACM SIGOPS Oper. Syst. Rev., 2010, vol. 44, no. 2, pp. 35–40.

    Article  Google Scholar 

  32. Joishi, J. and Sureka, A., Vishleshan: Performance Comparison and Programming Process Mining Algorithms in Graph-Oriented and Relational Database Query Languages, 2015.

    Google Scholar 

  33. Rodriguez, M.A., The Gremlin graph traversal machine and language (invited talk), Proceedings of the 15th Symposium on Database Programming Languages, 2015, pp. 1–10.

    Google Scholar 

  34. Aho, A.V. and Corasick, M.J., Efficient string matching: An aid to bibliographic search, Commun. ACM, 1975, vol. 18, no. 6, pp. 333–340.

    Article  MathSciNet  MATH  Google Scholar 

  35. Al-Rfou, R., Perozzi, B., and Skiena, S., Polyglot: Distributed word representations for multilingual nlp, arXiv Preprint arXiv:1307.1662, 2013.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Systems Analysis, Computer Science and Control Federal Research Center, Russian Academy of Sciences, Moscow, 119333, Russia

    D. A. Devyatkin, R. E. Suvorov & I. V. Sochenkov

Authors
  1. D. A. Devyatkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. E. Suvorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. V. Sochenkov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. A. Devyatkin.

Additional information

Original Russian Text © D.A. Devyatkin, R.E. Suvorov, I.V. Sochenkov, 2016, published in Iskusstvennyi Intellekt i Prinyatie Reshenii, 2016, No. 1, pp. 37–46.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Devyatkin, D.A., Suvorov, R.E. & Sochenkov, I.V. An Information Retrieval System for Decision Support: An Arctic-Related Mass Media Case Study. Sci. Tech. Inf. Proc. 44, 329–337 (2017). https://doi.org/10.3103/S0147688217050033

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0147688217050033

Keywords

Search

Navigation