Top 10 Cited Applied Mathematics and Sciences: An International Journal (MathSJ) Research Papers by mathsjournal

TOP 10 WEB & SEMANTIC TECHNOLOGY PAPER Applied Mathematics and Sciences: An International Journal (MathSJ) ISSN : 2349 - 6223 https://airccse.com/mathsj/MathSJ.txt

Citation Count -31

RETOOLING OF COLOR IMAGING IN THE QUATERNION ALGEBRA Artyom M. Grigoryan and Sos S. Agaian Department of Electrical and Computer Engineering, University of Texas at San Antonio, USA ABSTRACT A novel quaternion color representation tool is proposed to the images and videos efficiently. In this work, we consider a full model for representation and processing color images in the quaternion algebra. Color images are presented in the threefold complex plane where each color component is described by a complex image. Our preliminary experimental results show significant performance improvements of the proposed approach over other well-known color image processing techniques. Moreover, we have shown how a particular image enhancement of the framework leads to excellent color enhancement (better than other algorithms tested). In the framework of the proposed model, many other color processing algorithms, including filtration and restoration, can be expressed.

KEYWORDS Image Color Analysis, Discrete Fourier Transform, Quaternion Fourier Transformation For More Details :- https://airccse.com/mathsj/papers/1314mathsj02.pdf Volume Link :- https://airccse.com/mathsj/vol1.html

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Proc. IASTED Int. Conf. Signal Processing Communication, Marbella, Spain. [40] A.M. Grigoryan, (2001) “https://www.researchgate.net/publication/3317920_2-D_and_1D_multipaired_transforms_Frequency-time_type_wavelets” IEEE Trans. on Signal Processing, vol. 49, no. 2, pp. 344-353. [41] A.M. Grigoryan and S.S. Agaian, (2014) “https://www.researchgate.net/publication/261176845_AlphaRooting_Method_of_Color_Image_Enhancement_by_Discrete_Quaternion_Fourier_Transf orm,” [9019-3], SPIE proceedings, 2014 Electronic Imaging: Image Processing: Algorithms and Systems XII, February 2-6, San Francisco, California. [42] M. Trivedi, A. Jaiswal, and V. Bhateja, (2012) “A novel HSV based image contrast measurement index,” in Proc. of the Fourth International Conference on Signal and Image processing 2012 (ICIP 2012), pp. 545-555. [43] W.K. Pratt, (2000) https://books.google.co.in/books?id=eoTSBQAAQBAJ&pg=PA721&lpg=PA721&dq=W.K .+Pratt,+(2000)+Digital+Image+Processing&source=bl&ots=LJaE32AJ9&sig=ACfU3U1pwXoTj0JQAjF5NxU2MCmrEq74Kw&hl=en&sa=X&ved=2ahUKE wiepNvK8Z3mAhUPzzgGHQnWBv8Q6AEwCXoECAoQAQ#v=onepage&q=W.K.%20Pr att%2C%20(2000)%20Digital%20Image%20Processing&f=false, New York: Wiley. [44] R.C. Gonzalez and R.E. Woods, (2002) https://pdfs.semanticscholar.org/15bd/427a1a5f9bc57a7f67fb1b1fc85c5bb39f46.pdf, 2nd Edition, Prentice Hall. [45] P. Denis, P. Carre, and C.F. Maloigne, (2007) “https://www.researchgate.net/publication/222828198_Spatial_and_spectral_Quaternionic_a pproaches_for_Colour_Images,” Computer Vision and Image Understanding, vol. 107, pp. 74-87. [46] S. Nercessian, K. Panetta, and S. Agaian, (2009) “https://www.researchgate.net/publication/221225961_A_Generalized_Set_of_Kernels_for_ Edge_and_Line_Detection,” Proc. SPIE, vol. 7245, 72450U. [47] E.J. Wharton, K. Panetta, and S.S. Agaian, (2007) “https://pdfs.semanticscholar.org/e9ed/5a83fefaa55de960fcaaf8f3b24339ad93ef.pdf,” Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, pp. 3346-– 3352, Montreal, 7-10. [48] K. Panetta, S. Qazi, and S. Agaian, (2008) “https://www.researchgate.net/publication/252350455_Techniques_for_detection_and_class ification_of_edges_in_color_images,” Proc. SPIE 6982, Mobile Multimedia/Image Processing, Security, and Applications 2008, 69820W; doi:10.1117/12.777703. [49] S. Nercessian. S.S. Agaian, and K.A. Panetta, (2012) “https://www.researchgate.net/publication/258713242_MultiScale_Image_Enhancement_Using_a_Second_Derivative-Like_Measure_of_Contrast,” Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 82950Q; doi:10.1117/12.906494. [50] S. Agaian, (1999) “https://www.semanticscholar.org/paper/Visual-morphology-

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AUTHORS Artyom M. Grigoryan is Associate Professor in the Electrical and Computer Engineering at the University of Texas, San Antonio. He is the author of three book s, three book-chapters, two patents, and many journal papers and specializing in the theory and application of fast Fourier transforms, image enhancement, computerized tomography, processing biomedical images, and image cryptography. Sos S. Agaian is Professor of Electrical and Computer Engineering at the University of Texas, San Antonio. He has seven books, 500 scientific papers, and holds 14 patents. He is a Fellow of the International Society for Photo-Optical Instrumentation Engineers and Fellow Imaging Sciences and Technology (IS\&T).

Citation Count â&#x20AC;&#x201C;17

IMAGE AND AUDIO SIGNAL FILTRATION WITH DISCRETE HEAP TRANSFORMS Artyom M. Grigoryan and Mehdi Hajinoroozi Department of Electrical and Computer Engineering, The University of Texas at San Antonio, Texas USA

ABSTRACT Filtration and enhancement of signals and images by the discrete signal-induced heap transform (DsiHT) is described in this paper. The basic functions of the DsiHT are orthogonal waves that are originated from the signal generating the transform. These waves with their specific motion describe a process of elementary rotations or Givens transformations of the processed signal. Unlike the discrete Fourier transform which performs rotations of all data of the signalon each stage of calculation, the DsiHT sequentially rotates only two components of the data and accumulates a heap in one of the components with the maximum energy. Because of the nature of the heap transform, if the signal under process is mixed with a wave which is similar to the signal-generator then this additive component is eliminated or vanished after applying the heap transformation. This property can effectively be used for noise removal, noise detection, and image enhancement. KEYWORDS: Heap Transform, Fourier Transform, Filtration, Image Enhancement For More Details :- https://airccse.com/mathsj/papers/1114mathsj01.pdf Volume Link :- https://airccse.com/mathsj/vol1.html

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[25] A.M. Grigoryan and N. Du, (2010) “https://www.researchgate.net/publication/258402785_TwoDimensional_images_in_frequencytime_representation_Direction_images_and_resolution_map,” Journal of Electronic

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[28] A.M. Grigoryan and M.M. Grigoryan, (2008) “https://www.researchgate.net/publication/238586821_Discrete_unitary_transforms_genera ted_by_moving_waves,” in book Computer and Simulation in Modern Science (Editor-inChief: Prof. Nikos Mastorakis), vol. 1, pp. 26-31, Mathematics and Computers in Science and Engineering, A series of Reference Books and Textbooks, WSEAS Press. [29] A.M. Grigoryan and M.M. Grigoryan, (2007) “https://airccse.com/mathsj/papers/1114mathsj01.pdf,” [6701-25] Proc. of the International Conference: Wavelets XII, SPIE: Optics+Photonics 2007, vol. 6701, 670125, pp. 27-29. [30] A.M. Grigoryan and K. Naghdali, (2009) “https://www.researchgate.net/publication/228962202_Fast_unitary_heap_transforms_theo ry_and_application_in_cryptography,” [7351-16], Proc. of International Conference Mobile Multimedia/Image Processing, Security, and Applications 2009, DSS09 SPIE Defense, Security, and Sensing. [31] K. Naghdali, R. Raghunath, and A.M. Grigoryan, (2009) “https://www.researchgate.net/publication/220755240_Fast_SignalInduced_Transforms_in_Image_Enhancement,” Proc. of IEEE International Conference on Systems, Man, and Cybernetics, pp. 565- 570. [32] A. McAndrew, (2004) https://edurev.in/studytube/An-Introduction-to-Digital-ImageProcessing-with-M/775d8d8a-e094-4ba1-882c-9ada31d9559b_p , School of Computer Science and Mathematics Victoria University of Technology.

AUTHORS Artyom M. Grigoryan received the MS degrees in mathematics from Yerevan State University (YSU), Armenia, USSR, in 1978, in imaging science from Moscow Institute of Physics and Technology, USSR, in 1980, and in electrical engineering from Texas A&M University, USA, in 1999, and Ph.D. degree in mathematics and physics from YUS, in 1990. In December 2000, he joined the Department of Electrical Engineering, University of Texas at San Antonio, where he is currently an Associate Professor. He is the author of three books, five book-chapters, two patents, and many journal papers and specializing in the theory and application of fast Fourier transform, tensor and paired transforms, unitary heap transforms, image enhancement, computerized tomography, processing biomedical images, and image cryptography. Mehdi Hajinoroozi received his BS in electrical engineering from University of Tehran and his MS in electrical engineering from Technical University of Darmstadt, Germany in 2011. Currently, he is doing his graduate studies in the University of Texas at San Antonio, USA, in the Department of Electrical and Computer Engineering. His current research interests include digital

signal and image processing.

Citation Count â&#x20AC;&#x201C;7

MODIFIED ALPHA-ROOTING COLOR IMAGE ENHANCEMENT METHOD ON THE TWO-SIDE 2-DQUATERNION DISCRETE FOURIER TRANSFORM AND THE 2-DDISCRETE FOURIER TRANSFORM Artyom M. Grigoryan 1, Aparna John 1 , Sos S. Agaian 2 1University of Texas at San Antonio, San Antonio,TX 78249, USA 2 City University of New York / CSI ABSTRACT Color in an image is resolved to 3 or 4 color components and 2-Dimages of these components are stored in separate channels. Most of the color image enhancement algorithms are applied channelby-channel on each image. But such a system of color image processing is not processing the original color. When a color image is represented as a quaternion image, processing is done in original colors. This paper proposes an implementation of the quaternion approach of enhancement algorithm for enhancing color images and is referred as the modified alpha-rooting by the two-dimensional quaternion discrete Fourier transform (2-D QDFT). Enhancement results of this proposed method are compared with the channel-by-channel image enhancement by the 2D DFT. Enhancements in color images are quantitatively measured by the color enhancement measure estimation (CEME), which allows for selecting optimum parameters for processing by thegenetic algorithm. Enhancement of color images by the quaternion based method allows for obtaining images which are closer to the genuine representation of the real original color. KEYWORDS Modified alpha-rooting, quaternion Fourier transform, color image enhancement For More Details :- https://airccse.com/mathsj/papers/4217mathsj01.pdf Volume Link :- https://airccse.com/mathsj/vol4.html

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[24] A.M. Grigoryan and S. Dursun, " https://www.researchgate.net/publication/4137204_Multiresolution_of_the_Fourier_transfo rm," in the Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 (ICASSP '05), vol. 4, pp. 577–580, March 18-23, 2005, Philadelphia, PA [25] A.M. Grigoryan and S.S. Agaian, “Tensor form of image representation: Enhancement by imagesignals https://www.researchgate.net/publication/241531703_Tensor_form_of_image_representati on_Enhancement_by_image-signals," [5014-26] (Image Processing: Algorithms and Systems II, Electronic Imaging, Science and Technology 2003, IS&T/SPIE 15 Annual Symposium, Santa Clara, CA, January 20-24, 2003) [26] A.M. Grigoryan, “https://www.researchgate.net/publication/3317920_2-D_and_1D_multipaired_transforms_Frequency-time_type_wavelets,” IEEE Trans. on Signal Processing, vol. 49, no. 2, 344–353 (2001) [27] S.S. Agaian, H.G. Sarukhanyan, K.O. Egiazarian, J. Astola, https://spie.org/publications/book/890094?SSO=1, 2011. [28] A.M. Grigoryan, K. Naghdali, “https://www.researchgate.net/publication/220145891_On_a_Method_of_Paired_Represen tation_Enhancement_and_Decomposition_by_Series_Direction_Images,” Journal of Mathematical Imaging and Vision, vol. 34, no. 2, 185–199 (2009) [29] F.T. Arslan and A.M. Grigoryan, " https://projet.liris.cnrs.fr/imagine/pub/proceedings/ICIP2007/pdfs/0100537.pdf," in Proceedings of the 14th IEEE International Conference on Image Processing, vol. 1, pp. 537-540, San Antonio, Texas, September 16-19, 2007. [30] F.T. Arslan, J.M. Moreno, and A.M. Grigoryan, " https://www.researchgate.net/publication/253572947_Paired_directional_transformbased_methods_of_image_enhancement," in Proceedings of SPIE – vol. 6246, Visual Information Processing XV, April 18-19, 2006. [31] A.M. Grigoryan and S.S. Agaian, “Color Enhancement and Correction for Camera Cell Phone Medical Images Using Quaternion Tools,” in Electronic Imaging Applications in Mobile Healthcare, J. Tang, S.S. Agaian, and J. Tan, Eds., SPIE Press, Bellingham, Washington, pp. 77-117 (2016). [32] A.M. Grigoryan, S.S. Agaian, “https://www.researchgate.net/publication/317880905_Alpha-Rooting_Method_of_Grayscale_Image_Enhancement_in_The_Quaternion_Frequency_Domain,” Proceedings of IS&T International Symposium, Electronic Imaging: Algorithms and Systems XV, 29 Jan.2 Feb., Burlingame, CA, 2017. [33] A.M. Grigoryan, A. John, S.S. Agaian, “https://www.researchgate.net/publication/314666478_Color_image_enhancement_of_med ical_images_using_alpha-rooting_and_zonal_alpha-rooting_methods_on_2-D_QDFT,” Proceedings of SPIE Medical Imaging Symposium, Image Perception, Observer Performance, and Technology Assessment, 12 - 13 Feb., Orlando, FL, 2017. [34] http://www.nasa.gov/mission_pages/mars/images/index.html

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AUTHORS Artyom M. Grigoryan received the PhD degree in Mathematics and Physics from Yerevan State University (1990). He is an associate Professor of the Department of Electrical Engineering in the College of Engineering, University of Texas at San Antonio. The author of four books, 9 book-chapters, 3 patents and more than 120 papers and specializing in the design of robust filters, fast transforms, tensor and paired transforms, discrete tomography, quaternion imaging, image encryption, processing biomedical images. Aparna John received her B. Tech. degree in Applied Electronics and Instrumentation from University of Calicut, India and M. Tech. degree in Electronics and Communication with specialization in Optoelectronics and Optical Communication from University of Kerala, India. Now, she is a doctoral student in Electrical Engineering at University of Texas at San Antonio. She is pursuing her research under the supervision of Dr. Artyom M. Grigoryan. Her research interests include image processing, color image enhancements, fast algorithms, quaternion algebra and quaternion transforms including quaternion Fourier transforms. She is a student member of IEEE and also a member of Eta Kappa Nu Honor Society, University of Texas San Antonio Sos S. Agaian is the Distinguished Professor at the City University of New York/CSI. Dr. Agaian received the Ph.D. degree in math and physics from the Steklov Institute of Mathematics, Russian Academy of Sciences, and the Doctor of Engineering Sciences degree from the Institute of the Control System, Russian Academy of Sciences. He has authored more than 500 scientific papers, 7 books, and holds 14 patents. His research interests are Multimedia Processing, Imaging Systems, Information Security, Artificial Intelligent, Computer Vision, 3D Imaging Sensors, Fusion, Biomedical and Health Informatics.

Citation Count â&#x20AC;&#x201C;6

AN EFFICIENT HEURISTIC ALGORITHM FOR FLEXIBLE JOB SHOP SCHEDULING WITH MAINTENANCE CONSTRAINTS Mohsen Ziaeeâ&#x2C6;&#x2014; Department of Industrial Engineering, University of Bojnord, 94531-55111 Bojnord, Iran

ABSTRACT A parallel restoration procedure obtained through a splitting of the signal into multiple signals by the paired transform is described. The set of frequency-points is divided by disjoint subsets, and on each of these subsets, the linear filtration is performed separately. The method of optimal Wiener filtration of the noisy signal is considered. In such splitting, the optimal filter is defined as a set of sub filters applied on the splitting-signals. Two new models of filtration are described. In the first model, the traditional filtration is reduced to the processing separately the splittingsignals by the shifted discrete Fourier transforms (DFTs). In the second model, the not shifted DFTs are used over the splitting-signals and sub filters are applied. Such simplified model for splitting the filtration allows for saving 2 â&#x2C6;&#x2019; 4(+ 1) operations of complex multiplication, for the signals of length = 2^, > 2. .

KEYWORDS Time-frequency analysis, signal reconstruction, paired transform, Wiener filtration For More Details :- https://airccse.com/mathsj/papers/1214mathsj01.pdf Volume Link :- https://airccse.com/mathsj/vol1.html

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[14] A.M. Grigoryan, “https://link.springer.com/article/10.1007/s10851-006-5150-0,” Journal MathematicalImaging and Vision, vol. 25, pp. 87-105, 2006. [15] A.M. Grigoryan, (2001) “https://www.researchgate.net/publication/3317920_2-D_and_1D_multipaired_transforms_Frequency-time_type_wavelets,” Signal Processing, IEEE Transactions on, vol. 49, no. 2, pp. 344-353. [16] P. Patel, N. Ranganath, and A.M. Grigoryan, (2011) “https://www.researchgate.net/publication/272897980_Performances_of_Texas_instrument s_DSP_and_xilinx_FPGAs_for_Cooley-Tukey_and_Grigoryan_FFT_algorithms,” Journal of Engineering Technology, vol. 1, p. 83. [17] A.M. Grigoryan and M.M. Grigoryan, (2009https://www.researchgate.net/publication/259466566_Brief_Notes_in_Advanced_DS P_Fourier_Analysis_with_MATLAB, CRC Press Taylor and Francis Group.

AUTHORS Artyom M. Grigoryan received the MS degrees in mathematics from Yerevan State University (YSU), Armenia, USSR, in 1978, in imaging science from Moscow Institute of Physics and Technology, USSR, in 1980, and in electrical engineering from Texas A&M University, USA, in 1999, and Ph.D. degree in mathematics and physics from YUS, in 1990. In December 2000, he joined the Department of Electrical Engineering, University of Texas at San Antonio, where he is currently an Associate Professor. SM of IEEE since 1998. He is the author of three books, three book-chapters, two patents, and many journal papers and specializing in the theory and application of fast Fourier transforms, tensor and paired transforms, unitary heap transforms, image enhancement, computerized tomography, processing biomedical images, and image cryptography. Sree P.K. Devieni is a PhD student in the University of Texas at San Antonio in Electrical Engineering with a concentration in digital signal and image processing, transforms and wavelets.

Citation Count –6 NEW METHOD OF SIGNAL DENOISING BY THE PAIRED TRANSFORM

Artyom M. Grigoryan and Sree P.K. Devieni Department of Electrical Engineering, University of Texas at San Antonio, USA

KEYWORDS Time-frequency analysis, signal reconstruction, paired transform, Wiener filtration For More Details :-http://airccse.org/journal/ijwest/papers/0710ijwest01.pdf Volume Link :-http://www.airccse.org/journal/ijwest/vol1.html

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Citation Count –35

COMBINING ONTOLOGY DEVELOPMENT METHODOLOGIES AND SEMANTIC

WEB PLATFORMS FOR E-GOVERNMENT DOMAIN ONTOLOGY DEVELOPMENT Jean Vincent Fonou-Dombeu and Magda Huisman 1

Vaal University of Technology, South Africa and 2North-West University, South Africa

ABSTRACT One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engineering problems. However, current research practicing semantic development in e-government does not focus on the application of available methodologies and platforms for developing government domain ontologies. Furthermore, only a few of these researches provide detailed guidelines for developing semantic ontology models from a government service domain. This research presents a case study combining an ontology building methodology and two state-of-the-art Semantic Web platforms namely Protégé and Java Jena ontology API for semantic ontology development in e-government. Firstly, a framework adopted from the Uschold and King ontology building methodology is employed to build a domain ontology describing the semantic content of a government service domain. Thereafter, UML is used to semi-formally represent the domain ontology. Finally, Protégé and Jena API are employed to create the Web Ontology Language (OWL) and Resource Description Framework (RDF) representations of the domain ontology respectively to enable its computer processing. The study aims at: (1) providing egovernment developers, particularly those from the developing world with detailed guidelines for practicing semantic content development in their e-government projects and (2), strengthening the adoption of semantic technologies in e-government. The study would also be of interest to novice Semantic Web developers who might used it as a starting point for further investigations. KEYWORDS E-government, Semantic Web, Ontology, Java Jena API, Protégé, RDF, OWL For More Details :-http://airccse.org/journal/ijwest/papers/2211ijwest02.pdf Volume Link :-http://www.airccse.org/journal/ijwest/vol2.html

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T. Lee, C.T. Hon and D. Cheung, “XML Schema Design and Management for E-

government Data Interoperability,” Electronic Journal of E-government, Vol. 7, No. 4, pp. 381- 390, 2009. [2]

M. A. Wimmer, “Integrated Service Modelling for Online One-Stop Government,” Electronic Markets, Vol. 12, No. 3, pp.149-156, 2002.

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L. Sanati and J. Lu, “A Methodology Framework for E-government Service Delivery Integration, ” In Egovernment Interoperability Campus, Paris, France, 2007.

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ONTOLOGY MATCHING BASED ON HYPERNYM, HYPONYM, HOLONYM, AND MERONYM SETS IN WORDNET

JungAe Kwak and Hwan-Seung Yong, Ewha Womans University, Korea ABSTRACT Considerable research in the field of ontology matching has been performed where information sharing and reuse becomes necessary in ontology development. Measurement of lexical similarity in ontology matching is performed using synset, defined in WordNet. In this paper, we defined a Super Word Set, which is an aggregate set that includes hypernym, hyponym, holonym, and meronym sets in WordNet. The Super Word Set Similarity is calculated by the rate of words of concept name and synset’s words inclusion in the Super Word Set. In order to measure of Super Word Set Similarity, we first extractedm Matched Concepts(MC), Matched Properties(MP) and Property Unmatched Concepts(PUC) from the result of ontology matching. We compared these against two ontology matching tools – COMA++ and LOM. The Super Word Set Similarity shows an average improvement of 12% over COMA++ and 19% over LOM.

KEYWORDS Ontology Matching, Property Unmatched Concept, Semantic Relationship Set, Super Word Set Similarity For More Details :-http://airccse.org/journal/ijwest/papers/0410ijwest1.pdf Volume Link :-http://www.airccse.org/journal/ijwest/vol1.html

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ANALYZING THE IMPACT OF VISITORS ON PAGE VIEWS WITH GOOGLE ANALYTICS

Mohammad Amin Omidvar, Vahid Reza Mirabi and Narjes Shokry, Islamic Azad University, Iran ABSTRACT This paper develops a flexible methodology to analyze the effectiveness of different variables on various dependent variables which all are times series and especially shows how to use a time series regression on one of the most important and primary index (page views per visit) on Google analytic and in conjunction it shows how to use the most suitable data to gain a more accurate result. Search engine visitors have a variety of impact on page views which cannot be described by single regression. On one hand referral visitors are well-fitted on linear regression with low impact. On the other hand, direct visitors made a huge impact on page views. The higher connection speed does not simply imply higher impact on page views and the content of web page and the territory of visitors can help connection speed to describe user behavior. Returning visitors have some similarities with direct visitors.

KEYWORDS Internet, User studies, worldwide web, Systems analysis, Data mining, visitors behavior, web analysis, web metric, Google Analytics For More Details :-http://airccse.org/journal/ijwest/papers/0111ijwest02.pdf Volume Link :- http://www.airccse.org/journal/ijwest/vol2.html

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ONTOLOGY GUIDED INFORMATION EXTRACTION FROM UNSTRUCTURED TEXT

Raghu Anantharangachar, Srinivasan Ramani and S Rajagopalan, International Institute of Information Technology, India ABSTRACT In this paper, we describe an approach to populate an existing ontology with instance information present in the natural language text provided as input. An ontology is defined as an explicit conceptualization of a shared domain [18]. This approach starts with a list of relevant domain ontologies created by human experts, and techniques for identifying the most appropriate ontology to be extended with information from a given text. Then we demonstrate heuristics to extract information from the unstructured text and for adding it as structured information to the selected ontology. This identification of the relevant ontology is critical, as it is used in identifying relevant information in the text. We extract information in the form of semantic triples from the text, guided by the concepts in the ontology. We then convert the extracted information about the semantic class instances into Resource Description Framewor (RDF3) and append it to the existing domain ontology. This enables us to perform more precise semantic queries over the semantic triple store thus created. We have achieved 95% accuracy of information extraction in our implementation

KEYWORDS Ontology, Information Extraction, Knowledge Extraction, Semantic web, Ontology Based Information Extraction For More Details :-http://airccse.org/journal/ijwest/papers/4113ijwest02.pdf Volume Link :- http://www.airccse.org/journal/ijwest/vol4.html

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Citation Count –29

AGENTS AND OWL-S BASED SEMANTIC WEB SERVICE DISCOVERY WITH USER PREFERENCE SUPPORT

Rohallah Benaboud1, Ramdane Maamri2 and Zaidi Sahnoun2, 1

University of Oum El Bouaghi, Algeria and 2University of Constantine 2, Algeria

ABSTRACT Service-oriented computing (SOC) is an interdisciplinary paradigm that revolutionizes the very fabric of distributed software development applications that adopt service-oriented architectures (SOA) can evolve during their lifespan and adapt to changing or unpredictable environments more easily. SOA is built around the concept of Web Services. Although the Web services constitute a revolution in Word Wide Web, they are always regarded as non- autonomous entities and can be exploited only after their discovery. With the help of software agents, Web services are becoming more efficient and more dynamic. The topic of this paper is the development of an agent based approach for Web services discovery and selection in witch, OWL-S is used to describe Web services, QoS and service customer request. We develop an efficient semantic service matching which takes into account concepts properties to match concepts in Web service and service customer request descriptions. Our approach is based on an architecture composed of four layers: Web service and Request description layer, Functional match layer, QoS computing layer and Reputation computing layer. KEYWORDS Web service, discovery, agents, OWL-S & QoS. For More Details :-http://airccse.org/journal/ijwest/papers/4213ijwest06.pdf Volume Link :- http://www.airccse.org/journal/ijwest/vol4.html

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