Abstract
Semantic networks can simulate the human complex frames in reasoning process providing efficient association and inference mechanisms. Ontology can be used to fill the gap between human and computational intelligence for a task domain. For an evolving environment it is important 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. Folksonomies are a set of terms that a group of users tagged content without a controlled vocabulary. A Folksodriven Structure Network (FSN), built from the relations among the Folksodriven tags (FD tags), is presented as a folksonomy tags suggestions for the user to solve the problems inherent in an uncontrolled vocabulary of the folksonomy. 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. This paper discuss the deformation exhibiting linear behavior on FSN based on folksonomy tags chosen by different user on web site resources, this is a topic which has not been well studied so far. The discussion shows that the linear elastic constitutive equation possesses some leaning for the investigation. A constitutive law on FSN is investigated 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.
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A Real-time Control System (RCS) Reference Model Architecture, that implements a generic Hierarchical control system, was inspired 30 years ago by a theoretical model of the brain responsible for sensory-interactive and goal-directed control of conscious motions. Systems based on the RCS architecture have been designed and implemented to varying degrees for a wide variety of applications evolving through a variety of versions. Roughly each RCS consists of: Sensor, Controller, Actuator and Process (System) that should be controlled. (see http://www.nist.gov/el/isd/rcs.cfm).
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.
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).
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—Courant and Hilbert 2008).
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.
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.
Spinning consolidation: the growing of FD tags connections around the original FD tag.
Collapse: when links between FD tags shrink together abruptly and completely to a direct link with a main FD tag.
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
Antoniou G, Groth P, van Harmelen F, Hoekstra R (2012) A semantic Web Primer Ed. Cooperative Information Systems series
Bak P (1985) Symmetry, stability and elastic properties of icosahedral in commensurate crystals. Phys Rev B 32(9):5764–5772
Barabási A, Vazquez A, Oliveira J, Goh K, Kondor I, Dezso Z (2006) Modeling bursts and heavy tails in human dynamics. Phys Rev E 73(3):2006
Bezdek JC (1994) What is computational intelligence? Computational intelligence imitating life. IEEE Press, New York, pp 1–12
Born M, Huang K (1954) Dynamic theory of crystal lattices. Clarendon Press, Oxford
Calliard D (2006) Dislocation mechanism and plasticity of quasicrystals: TEM observations in icosahedral Al–Pd–Mn. Materials Sci Forum 509(1):49–56
Cattuto C, Schmitz C, Baldassarri A, Servedio VDP, Loreto V, Hotho A, Grahl M, Summe G (2007) Network properties of folksonomies. In: Proceedings of the WWW2007 International World Wide Web Conference
Chakrabarty J (2009) Theory of plasticity. Heinemann, Butterworth
Chen Z (2000) Computational intelligence for decision support. CRC Press, Boca Raton
Courant R, Hilbert D (2008) Methods of mathematical physics, vol II. Wiley, New York
Dal Mas M (2010) Ontology temporal evolution for multi-entity Bayesian Networks under Exogenous and Endogenous Semantic, CORR. Arxiv. (http://arxiv.org/abs/1009.2084)
Dal Mas M (2011a) Folksodriven Structure Network. Ontology Matching Workshop (OM-2011) collocated with the 10th International Semantic Web Conference (ISWC-2011), CEUR WS, vol 814. (http://ceur-ws.org/Vol-814). CORR. Arxiv. (http://arxiv.org/abs/1109.3138)
Dal Mas M (2011b) Cluster analysis for a scale-free Folksodriven Structure Network. Accepted for the International Conference on social computing and its applications (SCA 2011). CORR. Arxiv. (http://arxiv.org/abs/1112.4456)
Dal Mas M (2012a) Elasticity on ontology matching of Folksodriven Structure Network. Accepted for the 4th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2012). Kaohsiung Taiwan ROC. CORR. Arxiv. (http://arxiv.org/abs/1201.3900)
Dal Mas M (2012b) Intelligent interface architectures for Folksonomy Driven Structure Network. In: Proceedings of the 5th International Workshop on Intelligent Interfaces for Human-Computer Interaction (IIHCI-2012), Palermo, Italy, 519–525. IEEE (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6245653). doi:10.1109/CISIS.2012.158
Damme CV, Hepp M, Siorpaes K (2007) FolksOntology: an integrated approach for turning Folksonomies into ontologies. In: Proceedings of the ESWC Workshop Bridging the Gap between Semantic Web and Web 2.0, pp 57–70
Ding DH, Yang WG, Hu CZ (1993) Generalized elastic theory of quasicrystals. PhysRev B 48(10):7003–7010
Duch W (2007) Towards comprehensive foundations of computational intelligence. Challenges for computational intelligence. Springer, Berlin
García-Silva A, Corcho O, Alani H, Gómez-Pérez A (2012) Review of the state of the art: discovering and associating semantics to tags in folksonomies. J Knowl Eng Rev Arch 27:57–85
Grandi F (2012) Introducing an annotated bibliography on temporal and evolution aspects in the Semantic Web ACM. SIGMOD Record 41(4):18–21
Grätzer GA (2003) General lattice theory. Birkhäuser, Basel
Grunbaum B, Shephard GC (2011) Tilings and Patterns. Dover Publications, New York
Han W, Reddy BD (1995) Computational plasticity: the variational basis and numerical analysis. Comput Mech Adv 2:283–400
Hitzler P, Krötzsch M, Rudolph S (2011) Foundations of Semantic Web Technologies Ed. Chapman and Hall/CRC Textbooks in Computing
Jacob EK (2004) Classification and categorization: a difference that makes a difference. Library Trends. Citeseer 52(3):515–540
Levine D, Steinhardt PJ (1984) Quasicrystals: a new class of ordered structures. PhysRev 53:2477–2480
Lu PJ, Steinhardt PJ (2007) Decagonal and quasi-crystalline tilings in medieval Islamic architecture. Science 315(2007):1106–1110
McCarthy J (2005) The future of AI–A manifesto. AI Magazine 26:39
Merholz P (2004) Metadata for the masses. http://adaptivepath.com/ideas/e000361
Mussel L (1964) Conformazione e struttura di bio-polimeri. Dip. di Scienze Chimiche, Università di Padova, Padova Digital University Archive
Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC (2011) Detecting novel associations in large data sets. Science 334(6062):1518–1524. doi:10.1126/science.1205438. (http://www.sciencemag.org/content/334/6062/1518.full)
Sands DE (2002) Vectors and tensors in crystallography. Dover Publications, New York
Simo JC, Ju JW (1986) Strain- and stress-based continuum damage models-II. Computational aspects Department of Mechanical Engineering, Stanford University
Steurer W, Deloudi S (2009) Crystallography of quasicrystals. Springer, Berlin
Thurner S, Kyriakopoulos F, Tsallis C (2007) Unified model for network dynamics exhibiting nonextensive statistics. Phys Rev 76(3 Pt 2):036111
Trant J (2009) Studying social tagging and folksonomy: a review and framework. J Digit Inf 10(1):1–44
van der Maaten LJP, Postma EO, van den Herik HJ (2009) Dimensionality reduction: a comparative review Tilburg University Technical Report, TiCC-TR 2009-005
van Melkebeek D (2010) Special issue “Conference on computational complexity 2010”. Comput Complex 20(2):173–175. doi:10.1007/s00037-011-0008-2
Acknowledgments
I would like to thank Prof. Marco Colombetti of the “Department of Electronics, Information and Bioengineering” (DEIB) of the “Politecnico di Milano” University (Italy) for his advice on Knowledge Engineering and Artificial Intelligence. I am especially indebted to all the reviewers’ detailed comments and constructive suggestions on the manuscript.
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Dal Mas, M. Elastic Adaptive Dynamics Methodology on Ontology Matching on Evolving Folksonomy Driven Environment. Evolving Systems 5, 33–48 (2014). https://doi.org/10.1007/s12530-013-9086-5
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DOI: https://doi.org/10.1007/s12530-013-9086-5