Ranking based ontology scheming using eigenpair computation

Ontology, which is regularly used in computer information retrieval and other computer applications, plays an essential role in effectively retrieving the concepts that have highly semantic similarity with the original query concept. Meanwhile, ontology also returns the results to the user. Ontology mapping is used to connect the relationship between different ontologies, and similarity computation is the essence of such applications. In this article, we present a ranking based ontology optimizing algorithm to get the ontology score function which map each ontology vertex to a real number, and the similarity between ontology vertices is determined according to the difference of the scores. The ontology framework is designed relied on the eigenpair computation, and the solution is obtained by means of operator calculation. The result data of simulation experiment implies that our new ontology method has high efficiency and accuracy in ontology similarity measure and ontology mapping in multiple disciplines.

[1]  David Hawking,et al.  Overview of the TREC 2003 Web Track , 2003, TREC.

[2]  P. Zizler.  The spectral radius of a multivariate sampling operator , 2004 .

[3]  Hidemitsu Ogawa,et al.  Optimal Sampling Operator for Signal Restoration in the Presence of Signal Space and Observation Space Noises , 2005, IEICE Trans. Inf. Syst..

[4]  Yoon Mi Hong,et al.  Irregular and multi-channel sampling of operators , 2009, 0903.1082.

[5]  Wei Gao,et al.  Ontology Similarity Measure and Ontology Mapping Via Fast Ranking Method , 2011 .

[6]  Lan Mei-hui.  Ontology Mapping Algorithm Based on Ranking Learning Method , 2011 .

[7]  Filip De Turck,et al.  Ontology-Driven Monitoring of Patient's Vital Signs Enabling Personalized Medical Detection and Alert , 2011, MobiHealth.

[8]  Ingo Steinwart,et al.  Mercer’s Theorem on General Domains: On the Interaction between Measures, Kernels, and RKHSs , 2012 .

[9]  M. H. Castro,et al.  Integral operators generated by Mercer-like kernels on topological spaces , 2012 .

[10]  V. A. Menegatto,et al.  Reproducing properties of differentiable Mercer‐like kernels , 2012 .

[11]  V. A. Menegatto,et al.  Reproducing Properties of Differentiable Mercer-Like Kernels on the Sphere , 2012 .

[12]  Wei Gao,et al.  Ontology Similarity Measure by Optimizing NDCG Measure and Application in Physics Education , 2012 .

[13]  Zhen Li,et al.  Ontology Representation of Online Shopping Customers Knowledge in Enterprise Information , 2013 .

[14]  Jun Fan,et al.  Learning theory approach to minimum error entropy criterion , 2012, J. Mach. Learn. Res..

[15]  Amar Koleti,et al.  GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework , 2013, Bioinform..

[16]  Jian Yu,et al.  User-centric social context information management: an ontology-based approach and platform , 2013, Personal and Ubiquitous Computing.

[17]  L. Liang,et al.  Diffusion and harmonic analysis on hypergraph and application in ontology similarity measure and ontology mapping , 2013 .

[18]  Ling Liu,et al.  A Graph Derivation Based Approach for Measuring and Comparing Structural Semantics of Ontologies , 2014, IEEE Transactions on Knowledge and Data Engineering.

[19]  Damian Smedley,et al.  The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data , 2014, Nucleic Acids Res..

[20]  Álvaro Alesanco Iglesias,et al.  Designing an Architecture for Monitoring Patients at Home: Ontologies and Web Services for Clinical and Technical Management Integration , 2014, IEEE Journal of Biomedical and Health Informatics.

[21]  Miguel A. Sanz-Bobi,et al.  Food Track & Trace ontology for helping the food traceability control , 2014 .

[22]  Mathias Uslar,et al.  Ontology matching system for future energy smart grids , 2014, Eng. Appl. Artif. Intell..

[23]  Wei Gao,et al.  Minimax Learning Rate for Multi-dividing Ontology Algorithm ⋆ , 2014 .

[24]  Wei Gao,et al.  Linear Statistical Analysis of Multi-dividing Ontology Algorithm ⋆ , 2014 .

[25]  José Luis Rojo-Álvarez,et al.  Spectrally adapted Mercer kernels for support vector nonuniform interpolation , 2014, Signal Process..

[26]  Wei Gao,et al.  Gradient Learning Algorithms for Ontology Computing , 2014, Comput. Intell. Neurosci..

[27]  D. Costarelli,et al.  Order of approximation for sampling Kantorovich operators , 2014 .

[28]  Zoran Budimac,et al.  An overview of ontologies and data resources in medical domains , 2014, Expert Syst. Appl..

[29]  Gianluca Vinti,et al.  Applications of sampling Kantorovich operators to thermographic images for seismic engineering , 2014, ArXiv.

[30]  John D. Garofalakis,et al.  Hierarchical Itemspace Rank: Exploiting hierarchy to alleviate sparsity in ranking-based recommendation , 2015, Neurocomputing.

[31]  Dae-Won Kim,et al.  Fast multi-label feature selection based on information-theoretic feature ranking , 2015, Pattern Recognit..

[32]  Mahdi Karbasian,et al.  The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods , 2015, Expert Syst. Appl..

[33]  Wei Gao,et al.  New Multi-dividing Ontology Learning Algorithm Using Special LossFunctions , 2015 .

[34]  Damiano Rossello,et al.  Ranking of investment funds: Acceptability versus robustness , 2015, Eur. J. Oper. Res..

[35]  Ali Ebrahimnejad,et al.  A duality approach for solving bounded linear programming problems with fuzzy variables based on ranking functions and its application in bounded transportation problems , 2015, Int. J. Syst. Sci..

[36]  A. M. Anderson.  A Monte Carlo comparison of alternative methods of maximum likelihood ranking in racing sports , 2015 .

[37]  Patrick Pérez,et al.  Explicit Embeddings for Nearest Neighbor Search with Mercer Kernels , 2015, Journal of Mathematical Imaging and Vision.

[38]  Wei Gao,et al.  Ontology optimization tactics via distance calculating , 2016 .

[39]  Wei Gao,et al.  Ontology Similarity Measure and Ontology Mapping via Learning Optimization Similarity Function , .