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10.1007/s13740-014-0047-3 http://scihub22266oqcxt.onion/10.1007/s13740-014-0047-3 C4454427!4454427!26052350     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Data+Semant 2015 ; 4 (2): 117-32 Nephropedia Template TP {{tp|p=26052350|t=2015. Analysis and Prediction of User Editing Patterns in Ontology Development Projects |pdf=|usr=}}{{26052350}} gab.com Text Analysis and Prediction of User Editing Patterns in Ontology Development Projects JO: J Data Semant http://www.kidney.de/pget.php?&tw=1&pmid=26052350 #FOAvip The development of real-world ontologies is a complex undertaking, commonly involving a group of domain experts with different expertise that work together in a collaborative setting. These ontologies are usually large scale and have complex structures. To assist in the authoring process, ontology tools are key at making the editing process as streamlined as possible. Being able to predict confidently what the users are likely to do next as they edit an ontology will enable us to focus and structure the user interface accordingly and to facilitate more efficient interaction and information discovery. In this paper, we use data mining, specifically the association rule mining, to investigate whether we are able to predict the next editing operation that a user will make based on the change history. We simulated and evaluated continuous prediction across time using sliding window model. We used the association rule mining to generate patterns from the ontology change logs in the training window and tested these patterns on logs in the adjacent testing window. We also evaluated the impact of different training and testing window sizes on the prediction accuracies. At last, we evaluated our prediction accuracies across different user groups and different ontologies. Our results indicate that we can indeed predict the next editing operation a user is likely to make. We will use the discovered editing patterns to develop a recommendation module for our editing tools, and to design user interface components that better fit with the user editing behaviors. Twit Text FOAVip Analysis and Prediction of User Editing Patterns in Ontology Development Projects ????J Data Semant http://www.kidney.de/pget.php?&tw=1&pmid=26052350 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26052350 #FOAvip English Wikipedia <ref>{{Cite pmid|26052350}}</ref> Analysis and Prediction of User Editing Patterns in Ontology Development Projects #MMPMID26052350 Wang H; Tudorache T; Dou D; Noy NF; Musen MA J Data Semant 2015[Jun]; 4 (2): 117-32 PMID26052350show ga The development of real-world ontologies is a complex undertaking, commonly involving a group of domain experts with different expertise that work together in a collaborative setting. These ontologies are usually large scale and have complex structures. To assist in the authoring process, ontology tools are key at making the editing process as streamlined as possible. Being able to predict confidently what the users are likely to do next as they edit an ontology will enable us to focus and structure the user interface accordingly and to facilitate more efficient interaction and information discovery. In this paper, we use data mining, specifically the association rule mining, to investigate whether we are able to predict the next editing operation that a user will make based on the change history. We simulated and evaluated continuous prediction across time using sliding window model. We used the association rule mining to generate patterns from the ontology change logs in the training window and tested these patterns on logs in the adjacent testing window. We also evaluated the impact of different training and testing window sizes on the prediction accuracies. At last, we evaluated our prediction accuracies across different user groups and different ontologies. Our results indicate that we can indeed predict the next editing operation a user is likely to make. We will use the discovered editing patterns to develop a recommendation module for our editing tools, and to design user interface components that better fit with the user editing behaviors. äAnalysis and Prediction of User Editing Patterns in Ontology Developme(epmc).pdf DeepDyve Pubget Overpricing