Models for Supporting of Problem-Oriented Knowledge Search and Processing

In terms of search procedures and problem-oriented knowledge processing, complexity of identification and usage of key information is increasing constantly. A suggested hypothesis is based on the following statement: one of the ways to solve this problem is the improvement of semantic models for interpretation and using metadata of already-existing search profiles, pursuing similar aims, as prior data. We researched case-based reasoning in semantic search relating to knowledge filter. Concrete scientific results are: agent model, metamodel and case-model of knowledge filter which can solve problems of semantic identification of key information and processing of heterogeneous knowledge resources on the basis of ontology-based structures.

[1]  Qing He,et al.  Combination Methodologies of Multi-agent Hyper Surface Classifiers: Design and Implementation Issues , 2007, AIS-ADM.

[2]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  V. V. Kureichik,et al.  Knowledge management based on multi-agent simulation in informational systems , 2014, 2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT).

[4]  Aldo Gangemi,et al.  An Overview of the ONIONS Project: Applying Ontologies to the Integration of Medical Terminologies , 1999, Data Knowl. Eng..

[5]  David Amerland,et al.  Google Semantic Search: Search Engine Optimization (SEO) Techniques That Get Your Company More Traffic, Increase Brand Impact, and Amplify Your Online Presence , 2013 .

[6]  Larry Kerschberg,et al.  Emergent Semantics in Knowledge Sifter: An Evolutionary Search Agent Based on Semantic Web Services , 2006, J. Data Semant..

[7]  V. V. Bova,et al.  Decision Support Systems for Knowledge Management , 2015, CSOC.

[8]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[9]  Russell C. Eberhart,et al.  Recent advances in particle swarm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[10]  Tiago Ferra de Sousa,et al.  Particle Swarm based Data Mining Algorithms for classification tasks , 2004, Parallel Comput..