Skip to main content
SpringerLink
Log in

Hyperelastic-Based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment

  • Chapter
  • First Online:
Sentiment Analysis and Ontology Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 639))

Abstract

Due to the rapid growth of structured/unstructured and user-generated data (e.g., social media sites) volume of data is becoming too big or it moves too fast or it exceeds current processing capacity and so traditional data processing applications are inadequate. Computational Intelligence with Concept-based approaches can detect sentiments analyzing the concept based on text expressions without analyzing the singlef words as in the purely syntactical techniques. On human-centric intelligent systems Semantic networks can simulate the human complex frames in a reasoning process providing efficient association and inference mechanisms, while ontology can be used to fill the gap between human and Computational Intelligence for a task domain. For an evolving environment it is necessary to understand what knowledge is required for a task domain with an adaptive ontology matching. To reflect the evolving knowledge this paper considers ontologies based on folksonomies according to a new concept structure called “Folksodriven” to represent folksonomies. To solve the problems inherent an uncontrolled vocabulary of the folksonomy it is presented a Folksodriven Structure Network (FSN): a folksonomy tags suggestions built from the relations among the Folksodriven tags (FD tags). It was observed that the properties of the FSN depend mainly on the nature, distribution, size and the quality of the reinforcing FD tags. So, the studies on the transformational regulation of the FD tags are regarded to be important for an adaptive folksonomies classifications in an evolving environment used by Intelligent Systems to represent the knowledge sharing. The chapter starts from the discussion on the deformation exhibiting linear behavior on FSN based on folksonomy tags chosen by different user on web site resources. Then it’s formulated a constitutive law on FSN investigating towards a systematic mathematical analysis on stress analysis and equations of motion for an evolving ontology matching on an environment defined by the users’ folksonomy choice. The adaptive ontology matching and the elastodynamics are merged to obtain what we can call the elasto-adaptive-dynamics methodology of the FSN. Furthermore it is shown the last development defining a hyperelastic dynamic considering the internal folksonomy behavior of the stress and strain from original to deformed configuration.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    “folks” derives from the Old English “folc” (meaning ’people’), of Old High Germanic origin “folc”, related to German “volk”; while “taxonomy” derives from the Greek words “taxis” (meaning ‘order’, ’arrangement’) and “nomos” (‘law’ or ‘science’).

  2. 2.

    Nonlinear means that output isn’t directly proportional to input, or that a change in one variable doesn’t produce a proportional change or reaction in the related variable(s). In other words, a system’s values at one time aren’t proportional to the values at an earlier time.

  3. 3.

    A dynamical system is anything that moves, changes, or evolves in time. Hence, chaos deals with what the experts like to refer to as dynamical-systems theory (the study of phenomena that vary with time) or nonlinear dynamics (the study of nonlinear movement or evolution).

  4. 4.

    Quasiperiodicity in the general definition also includes incommensurately modulated FSN as well as composite FSN. Here, we will not discuss these cases, which either can be seen as periodic modification of an underlying basic structure or as a kind of intergrowth of periodic structures.

  5. 5.

    Spinning consolidation: the growing of FD tags connections around the original FD tag.

  6. 6.

    Collapse: when links between FD tags shrink together abruptly and completely to a direct link with a main FD tag.

  7. 7.

    A phonon is a quantum mechanical definition of the lattice vibration that uniformly oscillates at the same frequency. It is known as the “normal mode” in classical mechanics. According to it any arbitrary lattice vibration can be described as a superposition of the elementary vibrations described by the phonon (cfr. Fourier analysis—[12]).

  8. 8.

    Similar to phonon, phason is associated with nodes of lattice motion, considered here as FD tags. However, whereas phonons are related to translation of FD tags, phasons are associated with FD tags rearrangements.

  9. 9.

    According to classical physic the Hooke’s law (law of elasticity), is depicted by \(F=-kx\) Where the movement of the end of the spring is expressed by x respect its equilibrium position. F depicts the spring restoring force and k is the spring (or rate) constant.

References

  1. Antoniou, G., Groth, P., van Harmelen, F., Hoekstra, R.: A Semantic Web Primer. Cooperative Information Systems series (2012)

    Google Scholar 

  2. Asur, S., Huberman, B.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1. IEEE (2010)

    Google Scholar 

  3. Bak, P.: Symmetry, stability and elastic properties of icosahedral in commensurate crystals. Phys. Rev. B 32(9), 5764–5772 (1985)

    Google Scholar 

  4. Barabási, A., Vazquez, A., Oliveira, J., Goh, K., Kondor, I., Dezso, Z.: Modeling bursts and heavy tails in human dynamics. Phys. Rev. E 73(3) (2006)

    Google Scholar 

  5. Bezdek, J.C.: What is computational intelligence? Computational Intelligence Imitating Life, pp. 1–12. IEEE Press, New York (1994)

    Google Scholar 

  6. Born, M., Huang, K.: Dynamic Theory of Crystal Lattices. Oxford Clarendon Press (1954)

    Google Scholar 

  7. Burnap, P., Gibson, R., Sloan, L., Southern, R.: 140 Characters to Victory?: Using Twitter to predict the UK 2015 General Election (2015). arXiv preprint arXiv:1505.01511

  8. Calliard, D.: Dislocation mechanism and plasticity of quasicrystals: TEM observations in icosahedral Al-Pd-Mn. Mater. Sci. Forum 509(1), 50–56 (2006)

    Google Scholar 

  9. Cattuto, C., Schmitz, C., Baldassarri, A., Servedio, V.D.P., Loreto, V., Hotho, A., Grahl, M., Summe, G.: Network properties of folksonomies. In: Proceedings of the WWW2007 International World Wide Web Conference (2007)

    Google Scholar 

  10. Chakrabarty, J.: Theory of plasticity. Butterworth-Heinemann (2009)

    Google Scholar 

  11. Chen, Z.: Computational Intelligence for Decision Support. CRC Press (2000)

    Google Scholar 

  12. Courant, R., Hilbert, D.: Methods of Mathematical Physics, vol. 2. Interscience Wiley (2008)

    Google Scholar 

  13. Dal Mas, M.: Elastic adaptive dynamics methodology on ontology matching on evolving folksonomy driven environment. Evolving Syst. J. 5(1), 33–48 (2014). (http://link.springer.com/article/10.1007%2Fs12530-013-9086-5)

    Google Scholar 

  14. Dal Mas, M.: Cluster analysis for a scale-free folksodriven structure network. Accepted for the International Conference on Social Computing and its Applications (SCA 2011) (2011). http://arxiv.org/abs/1112.4456

  15. Dal Mas, M.: Elastic adaptive ontology matching on evolving folksonomy driven environment In: Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems EAIS 2012, pp. 35–40. Madrid, Spain (2012)

    Google Scholar 

  16. Dal Mas, M.: Elasticity on ontology matching of folksodriven structure network. Accepted for the CIIDS 2012—Kaohsiung Taiwan R.O.C. (2012). http://arxiv.org/abs/1201.3900

  17. Dal Mas, M.: Folksodriven Structure Network. In: Ontology Matching Workshop (OM-2011) collocated with the 10th International Semantic Web Conference (ISWC-2011), CEUR WS, vol. 814 (2011). (http://arxiv.org/abs/1109.3138), (http://ceur-ws.org/Vol-814/)

  18. Dal Mas, M.: Intelligent interface architectures for folksonomy driven structure net. In: Proceedings of the 5th International Workshop on Intelligent Interfaces for Human-Computer Interaction (IIHCI-2012), pp. 519–525. IEEE, Palermo, Italy (2012). doi:10.1109/CISIS.2012.158, (http://ieeexplore.ieee.org/876 xpl/articleDetails.jsp?arnumber=6245653)

  19. Dal Mas, M.: Layered ontological image for intelligent interaction to extend user capabilities on multimedia systems in a folksonomy driven environment. In: Smart Innovation, Systems and Technologies, vol. 36, pp. 103–122. Springer (2015). doi:10.1007/978-3-319-17744-1_7, (http://link.springer.com/chapter/10.1007%2F978-3-319-17744-1_7)

    Google Scholar 

  20. Dal Mas, M.: Ontology Temporal Evolution for Multi-entity Bayesian Networks under Ex-ogenous and Endogenous Semantic (2010). http://arxiv.org/abs/1009.2084

  21. Damme, C.V., Hepp, M., Siorpaes, K.: FolksOntology. In: Proceedings of the ESWC Workshop ’Bridging the Gap between Semantic Web and Web 2.0, pp. 57–70 (2007)

    Google Scholar 

  22. Ding, D.H., Yang, W.G., Hu, C.Z.: Generalized elastic theory of quasicrystals. Phys. Rev. B 48(10), 7003–7010 (1993)

    Article  Google Scholar 

  23. Duch, W.: Towards comprehensive foundations of computational intelligence. Challenges for Computational Intelligence. Springer, Berlin (2007)

    Chapter  Google Scholar 

  24. García-Silva, A., Corcho, O., Alani, H., Gómez-Pérez, A.: Review of the state of the art: discovering and associating semantics to tags in folksonomies. J. Knowl. Eng. Rev. Arch. 27(1), 57–85 (2012)

    Article  Google Scholar 

  25. Grandi, F.: Introducing an annotated bibliography on temporal and evolution aspects in the semantic web. ACM SIGMOD Rec. 41(4) (2012)

    Google Scholar 

  26. Grätzer, G.A.: General Lattice Theory. Birkhäuser, Basel (2003)

    MATH  Google Scholar 

  27. Grunbaum, B., Shephard, G.C.: Tilings and Patterns. Dover Publications (2011)

    Google Scholar 

  28. Han, W., Reddy, B.D.: Computational plasticity: the variational basis and numerical analysis. Comput. Mech. Adv. 2(4) (1995)

    Google Scholar 

  29. Hashiguchi, K.: Elastoplasticity Theory. Springer (2013)

    Google Scholar 

  30. Hill, R.: On constitutive inequalities for simple materials. Int. J. Mech. Phys. Solids 16(1968), 229–242 (1968)

    Article  MATH  Google Scholar 

  31. Hitzler, P., Krötzsch, M., Rudolph, S.: Foundations of Semantic Web Technologies. Chapman & Hall/CRC Textbooks in Computing (2011)

    Google Scholar 

  32. Jacob, E.K.: Classification and categorization: a difference that makes a difference. Libr. Trends 52(3), 515–540 (2004)

    Google Scholar 

  33. Kitchin, R.: Big data, new epistemologies and paradigm shifts. Big Data & Society 1.1, 2053951714528481 (2014)

    Google Scholar 

  34. Levine, D., Steinhardt, P.J.: Quasicrystals: a new class of ordered structures. Phys. Rev. 53, 2477–2480 (1984)

    Google Scholar 

  35. Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press (2005)

    Google Scholar 

  36. Lu, P.J., Steinhardt, P.J.: Decagonal and quasi-crystalline tilings in medieval islamic architecture. Science 315(2007), 1106–1110 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  37. McCarthy, J.: The future of AI-A manifesto. AI Magazine 26 (2005)

    Google Scholar 

  38. Merholz, P.: Metadata for the masses (2004). http://adaptivepath.com/ideas/e000361

  39. Mussel, L.: Conformazione e struttura di bio-polimeri. Dip. di Scienze Chimiche—Università di Padova—Padova Digital University Archive (1964)

    Google Scholar 

  40. Ostermann, S., Iosup, A., Yigitbasi, M.N., Prodan, R., Fahringer, T., Epema, D.: An early performance analysis of cloud computing services for scientific computing. In: CloudComp, LNICST, vol. 34, pp. 1–10, (2009)

    Google Scholar 

  41. Reshef, D.N., Reshef, Y.A., Mitzenmacher, M., Sabeti, P.C.: Detecting novel associations in large data sets. Science 334(6062), 1518–1524 (2011). doi:10.1126/science.1205438

    Google Scholar 

  42. Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S.M., Ritter, A., Stoyanov, V.: SemEval-2015 task 10. In: Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval ’2015, Denver, Colorado, June 2015

    Google Scholar 

  43. Ruths, D., Pfeffer, J.: Social media for large studies of behavior. Science 346(6213), 1063–1064 (2014)

    Google Scholar 

  44. Sands, D.E.: Vectors and tensors in crystallography. Dover Publications (2002)

    Google Scholar 

  45. Simo, J.C., Ju, J.W.: Strain- and stress-based continuum damage models–II. Computational aspects Department of Mechanical Engineering, Stanford University Press (1986)

    Google Scholar 

  46. Steurer, W., Deloudi, S.: Crystallography of Quasicrystals. Springer (2009)

    Google Scholar 

  47. Thurner, S., Kyriakopoulos, F., Tsallis, C.: Unified model for network dynamics exhibiting nonextensive statistics. Phys. Rev. (2007)

    Google Scholar 

  48. Trant, J.: Studying social tagging and folksonomy: a review and framework. J. Digit. Inf. 10(1) (2009)

    Google Scholar 

  49. van der Maaten, L.J.P., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: a comparative review. Tilburg University Technical report, TiCC-TR 2009-005 (2009)

    Google Scholar 

  50. van Melkebeek, D.: Special Issue “Conference on Computational Complexity 2010”. Computational Complexity vol. 20, no. 2, pp. 173–175. Springer, Basel (2010)

    Google Scholar 

  51. White, C.: Using big data for smarter decision making. BI research (2011)

    Google Scholar 

  52. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment Analysis. In: Proceedings of HLT-EMNLP-2005 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Research and Development - Artificial Intelligence Unit technologos, via G. Mameli 4, 20129, Milano, Italy

    Massimiliano Dal Mas

Authors
  1. Massimiliano Dal Mas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Massimiliano Dal Mas .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Dal Mas, M. (2016). Hyperelastic-Based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30319-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30317-8

  • Online ISBN: 978-3-319-30319-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation