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Fig 4. Content and context awareness protocol ontology To understand each other correctly, accurate information semantic transportation is the first part. The core ontology of KC 2 A 2 P defines three concepts of content, context, act, which not only support the semantic interconnection between different protocols, but also offer framework for the protocol information formation. (Fig4) V. ONTOLOGY-BASED AWARENESS COMPUTING Based on KC 2 A 2 P ontology, senders can decide what it need to encode and receiver can know what the truth meaning of transferred message is and what is related the requirement. Ontology-based semantic relatedness computing is important for realizing this mutual awareness. Semantic relatedness is a general concept that could include semantic similarity computing. And it is more useful than just semantic similarity computing. Because similar entities are always semantically related by virtue of their similarity (female-woman), but dissimilar entities may also be semantically related by lexical relationships such as meronymy (car–wheel) and antonymy (hot–cold), or just by any kind of functional relationship or frequent association (pencil–paper, penguin–Antarctica, rain– flood). Computational applications typically require relatedness rather than just similarity; for example, when asking for traveling information, some resource about hotel and weather is also required. SRC (Algorithm of Semantic Relatedness of Concepts) [13] is a semantic relativity algorithm ,which just bases on ontology and does not need any former knowledge. It has three main steps : reading OWL files and building proper graph; according to its subsume axioms producing sub graph( that is a tree structure) consisting only subsumption relationship and counting the semantic relatedness by myObjectMatching method; according to the relation path functions ,calculating the whole classes semantic similarity based on the graph filling with six kinds of relations. Some formulas are: Sem _ Re lated [ C ][ C ] is the semantic 1 2 relativity value of C and C 1 2 Sem _ Weight [ C ][ C ] is the direct 2 3 semantic weight of C and C 2 3 Sem _ Road [ C ][ C ] = 1 3 Sem _ Re lated [ C ][ C ] × Sem _ Weight [ C ] [ C ] (1) 1 2 2 3 Sem _ Re lated [ C ][ C ] = max ⎛ _ [ ][ ][ ] ⎞ ⎜ Sem Road S C C ⎟ (2) 1 3 ⎝ 1 3 s ⎠ VI. EXPERIMENT We take Education Information Platform as experiment background, Eclipse+JADE (Java Agent Development framework) as exploitation tool, research on the awareness-based knowledge communication process. One client inquires a book named Semantic Web. There are four different providers. Provider1 holds one book on Ontology, provider2 holds Semantic Web Journal and provider3 hasn’t any resource about Semantic Web. When the buyer sends a request for looking for a book on Semantic Web, provider3 refuses the request because it doesnot have this resource. Provider1 and provider2 analyze the request by content awareness algorithm and send decision to the client. Client analyses these replies and chooses propose which is close to its own request. The interaction process of this example is illustrated by Figure 5. 180
ACKNOWLEDGMENT This work is funded by Shanxi Province under Grand No. 2006021015, Project of science and technology of ShanXi education department under Grant No.2007111. Fig5 one successful interaction process Fig 6 one failing interaction process The communication without extra content and context information failed (Fig6). By the comparison of interaction process in Figure 5 and Figure 6, we can find out that adding content and context-awareness mechanism to knowledge communication can greatly improve the quality of communication. VII. CONCLUSIONS This paper proposed awareness-based knowledge communication, build knowledge communication protocol stack and KC 2 A 2 P protocol ontology for content and context awareness. Based on such model,we utilze SRC algorithm to obtain the semantic relativity value and realize mutual awareness and understanding in communication. At last, some experiments are given to show awareness-based knowledge communication would improve the meaning delivery and optimize the communication quality. This article is part of our ongoing project. In the future, we will improve and fulfill the adaptive coding rule and carry out more research on intelligent software mechanism to enable the protocol architecture widely applied to web applications, agent-based systems, ubiquitous computing environment, and mobile communications and so on. REFERENCES [1] T. Finin, Y. Labrou, J. Mayfield, KQML as an agent communication language, in: J. Bradshaw (Ed.), Software Agents, MIT Press, Cambridge, MA, 1997. [2] Daniel R. Kuokka ,Jim McGuire . SHADE: Knowledge- Based Technology for the Re-Engineering Problem. http://www-ksl.stanford.edu/knowledgesharing/papers/shade-overview.html \#ref\_11.2009 [3] Kit-ying Hui, Alun Preece. An Infrastructure for Knowledge Communication in AKT. http://www.csd.abdn.ac.uk/~apreece/akt/aktbus/aktbus- 1.0/index.html 2003 [4] Bai Yan,Liu Dayou. Logical exception handling method in agent communication.[J] Computer Research and Development. Vol. 44, no. 3, pp. 427-433. Mar. 2007 [5] Juan Ignacio Vazquez and Diego López de Ipia. mRDP: An HTTP-based Lightweight Semantic Discovery Protocol. The International Journal of Computer and Telecommunications Networking. 2007. [6] Felix Fischer and Michael Rovatsos. Reasoning about communication: A practical approach based on empirical semantics. In Proceedings of the 8th International Workshop on Cooperative Information Agents (CIA-04), volume 3191 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 2004. [7] Darina Dicheva, Lora Aroyo. A Minimalist Approach to Support Ontology-driven Educational Information Systems Communication. International Workshop on Applications of Semantic Web Technologies for E- Learning (SW-EL). 2004 [8] Jesu′s Bermu′dez etc. Interoperation among agent-based information systems through a communication acts otology. Information systems 32 (2007)1121-1144 [9] Li wang,Liqing liu.Reserch on Content-awareness Protocol of Knowledge Communication Based on Ontology.Knowledge Discovery and Data Mining, WKDD2009. [10] Wang xiuling,Wang li,Feng xiufang,Yu xueli. Research on semantic web service protocol Soap based on ontology Chinese Artificial intelligence annual academic conference 2005:1 482-487. [11] Michal Laclavík. Ontology and Agent based Approach for Knowledge Management. Doctor Thesis. Institute of Informatics, Slovak <strong>Academy</strong> of Sciences.June 2005 [12] T. Berners-Lee, J. Hendler, O. Lassila, The semantic web ,Sci. Am. 284 (5) (2001) 34-43. [13] Wang, L. and X.L. Yu. A method measuring semantic relatedness for concepts in ontology base. in 4th International Conference on Information Technology and Applications, ICITA 2007, January 15, 2007 - 181
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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ISBN 978-952-5726-09-1 (Print) Proc
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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oth α and β . determination of Qu
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Strong earthquake 0.1< M L
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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Page 227 and 228: Ⅵ. CONCLUSION Figure 6. The Flask
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such as weather information, techno
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the opening window, a related page
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1) Process context storage areas. I
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V. CONCLUSION In this thesis, sCPU-
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main types of horizontal search env
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Enterprise Portal security provides
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() t = [ S () t S () t ] T N S ,...
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[2] Kumaravel, N., and Kavitha, V.,
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output, so BP network has been wide
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input to the artificial neural netw
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(2) In a process (tokens from outsi
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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