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Presentation in TUDelft 范正洁 Fan Zhengjie Personal Introduction MSc-thesis PhD Plan Work Personal Introduction MSc-thesis PhD Plan Work Personal Introduction I come from Bengbu, Anhui, China Education B.E.(major) of Computer Science, Anhui University, China, 2005 B.A.(minor) of English, Anhui University, China, 2005 M.E. of Applied Computer Technology, University of Science and Technology of China, China, 2008 Research Interests Artificial Intelligence and its application on the web Semantic Web Semantic Web Service Multi-Agent System Ontology Data Mining and Machine Learning Hobby Classical music Singing Yoga Playing the accordion Personal Introduction MSc-thesis PhD Plan Work MSc-thesis Work Two ranking methods in Discovery process of Semantic Web Service. RASC Algorithm RAUP Algorithm Background Two main directions of web improvement: Semantic Web: adding semantic on the web Web Service: providing automatic services online Semantic Web Service: combining technologies of both Semantic Web and Web Service to fulfill automatic computation Web Service lacks semantic support! Syntax only! Discovery Discovery is a very important process of SWS, which consists of two steps: Matching: comparing the function web user wants with the one web services provide; Ranking: putting all matched web services into an ordered list on certain criteria. Ranking should be worked on. Ranking criteria Serving Capability User Preference Service Quality RASC Algorithm Core idea: “rewriting”: meeting as much as web user’s output, asking as fewer as web user’s input Comparison on output—1st dimension ranking criteria Comparison on input—2nd dimension ranking criteria RASC Algorithm: Output comparison: Input comparison: Ranking: Example Output relationship: Input relationship: Ordered list: Discussion Time complexity is O(n2), Space complexity is O(n). Comparing input and output seperately Adding input comparison as part of ranking criteria, which reflects the interation requirement of web user. Experiment Tools: Logic reasoner RACER Coding language Java Server with processor AMD 4000+,memory 2G Discussion: Ranking step costs quite a few executing time RAUP Algorithm Core ideas: Drawing preference information by interacting with web users Putting on relative weights on properties and values Ranking on sum of weights Algorithm 1. Selecting pairs of web services randomly and sending them to web user to compare; 2. Classifying web services into two web service sets according to users' answers; 3. Extracting user preference from two web service sets by Apriori Algorithm; 4. Sending the drawn preference to the user to check; 5. Quantifying the checked preference by setting weights; 6. Computing each web service's sum of weights and ranking them into an ordered list. Example web services: Selected pairs: User preference: P1=Q11, P3=Q31 Classification: preferred web services set is {S2, S3, S1, S5, S5}, non-preferred web services set is {S1, S4, S3, S2, S4,}. Preferred set: Frequent properties: Non-preferred set: ∴no preference on properties Frequent values: Preferred set: Non-preferred set: Case 1: Case 2: After user checking, Case 1 is the best choice. Weights of properties: Weights of values: ∵ set ∴ Sum of weights: Result of ranking: Discussion Time complexity is O(r2), Space complexity is O(r). Drawing preference information by interaction Weighing properties and values on their contribution on discovery Fulfilling individual oriented. Experiment: Tools: Coding language Java Server with processor AMD 4000+, memory 2G Discussion: Low executing time Drawing majority preference Personal Introduction MSc-thesis PhD Plan Work Semantic Interoperability Three kinds of data interoperability: System interoperability Syntax and structure interoperability Semantic interoperability: heterogeneity Solution Ontology Process Mapping Mapping Building a basic domain ontology Merging other ontologies into the basic domain ontology Computing the relationship between two processes based on the same ontology Thank you!