Lightweight Context-Based Web-Service Composition Model for Mobile Devices

  • Roshan FernandesEmail author
  • G. L. Rio D’Souza
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 21)


With the widespread use of mobile devices having powerful processing capabilities, there is an ever-increasing demand to localize essential mobile services on these mobile devices to increase convenience. Most of the current computation concentrates on collecting service information from mobile devices and processing it at the server side. This is because semantic analysis of the service description in the mobile device is a resource intensive process. One of the main processes involved in the semantic analysis is the Parts of Speech (POS) Tagging. Currently, POS tools are not available for mobile devices. POS tagging however is a resource intensive application which is a challenge in the context of mobile devices due to limited availability of resources such as power, memory and processing capability. This chapter discusses a new, lightweight, context based web service composition model for mobile devices. The main idea is to build a lightweight POS tagger in the mobile device itself. The POS tagger finds its application in the context of identifying services requested by users in the form of natural language queries. Once the service names are identified in the mobile device, the request is sent to the web-service providers for their response and these responses are composed in the mobile device.


Mobile web-service Parts of speech tagger (POS) Markov chain probabilistic model JSON XML Trie 


  1. 1.
    Hasan FM, UzZaman N, Khan M (2007) Comparison of different POS tagging techniques (n-gram, HMM and Brill’s tagger) for bangla. In: Elleithy K (ed) Advances and innovations in systems, computing sciences and software engineering. Springer, DordrechtGoogle Scholar
  2. 2.
    Alva P, Hegde V (2016) Hidden Markov model for POS tagging in Word Sense Disambiguation. In: International conference on computational systems and information systems for sustainable solutionsGoogle Scholar
  3. 3.
    Diesner J, Part of speech tagging for english text data. School of Computer Science Carneige Mellon University Pittsburgh, PA 15213Google Scholar
  4. 4.
    Xiao J, Wang X, Liu B (2007) The study of a non-stationary maximum entropy Markov model and its application on the POS tagging task. ACM Trans Asian Lang Inf Process 6:2 (Article No. 7)CrossRefGoogle Scholar
  5. 5.
    Tursun E, Ganguly D, Osman T, Yang Y-T, Abdukerim G, Zhou J-L, Liu Q (2016) A semi-supervised tag-transition-bsed Markovian model for Uyghur Morphology analysis. ACM Trans Asian Low-Resour Lang Inf Process 16:2 (Article No. 8)CrossRefGoogle Scholar
  6. 6.
    Elahimanesh MH, Minaei-Bidgoli B, Kermani F (2014) ACUT: an associative classifier approach to unknown word POS tagging. In: Movaghar A, Jamzad M, Asadi H (eds) Artificial intelligence and signal processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, ChamGoogle Scholar
  7. 7.
    Lv C, Liu H, Dong Y (2010) An efficient corpus based part-of-speech tagging with GEP. In: Sixth international conference on semantics, knowledge and gridsGoogle Scholar
  8. 8.
    Rattenbury T, Naaman M (2009) Methods for extracting place semantics from flickr tags. ACM Trans Web 3:1 (Article 1)CrossRefGoogle Scholar
  9. 9.
    Hamzah A, Widyastuti N (2015) Document subjectivity and target detection in opinion mining using HMM-POS-tagger. In: International conference on information, communication technology and system (ICTS),Google Scholar
  10. 10.
    Yi C (2015) An english pos tagging approach based on maximum entropy. In: International conference on intelligent transportation, big data & smart city (ICITBS)Google Scholar
  11. 11.
    Piao S, Dallachy F, Baron A, Demmen J et al (2017) A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation. Comput Speech Lang 46:113–135CrossRefGoogle Scholar
  12. 12.
    Sun S, Liu H, Lin H (2012) Twitter part-of-speech tagging using pre-classification hidden markov model. In: IEEE international conference on systems, man, and cybernetics, 14–17 Oct 2012Google Scholar
  13. 13.
    Liu, C.-L., Hsaio W.-H, Lee C.-H, Lu G.-C, Jou E (2012) Movie rating and review summarization in mobile environment. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(3)CrossRefGoogle Scholar
  14. 14.
    Rathod S, Govilkar S (2015) Survey of various POS tagging techniques for Indian regional languages. Int J Comput Sci Inf Technol 6(3):2525–2529Google Scholar
  15. 15.
    Kadim A, Lazrek A (2016) Int J Speech Technol 19:303. Scholar
  16. 16.
    Brill E (1992) A simple rule-based part of speech tagger. In: Proceedings of the third conference on Applied natural language processingGoogle Scholar
  17. 17.
    Fonseca ER, Rosa JLG, Aluisio SM (2015) Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese. J Braz Comput Soc 21(2).
  18. 18.
    Xu Z, Zhang H, Sugumaran V, Raymond Choo K-K, Mei L, Zhu Y (2016) Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media. EURASIP J Wirel Commun NetwGoogle Scholar
  19. 19.
    Du M, Jing C, Du M ( 2016) Tag location method integrating GNSS and RFID technology. J Glob Position SystGoogle Scholar
  20. 20.
    Zhao L, Men J, Zhang C, Liu Q, Jiang W, Wu J, Chang Q (2010) A combination of statistical and rule-based approach for mongolian lexical analysis. In: 2010 international conference on asian language processingGoogle Scholar
  21. 21.
    Seyyed SR, Fakhrahmad M, Sadredini MH (2015) PTokenizer: POS tagger tokenizer. In: 2015 2nd international conference on knowledge-based engineering and innovation (KBEI)Google Scholar
  22. 22.
    Ratnaparki A (1996) A maximum entropy model for part-of-speech tagging. In: Proceedings of the conference on empirical methods in natural language processing, vol 1Google Scholar
  23. 23.
  24. 24.
  25. 25.

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNMAM Institute of TechnologyNitteIndia
  2. 2.Department of Computer Science and EngineeringSt. Joseph Engineering CollegeVamanjoor, MangaloreIndia

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