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Our main contributions are: (i) we design an entity relationship diagram for OPM and translate it into a relational database schema for the storage of provenance; (ii) we show that provenance reasoning defined in OPM (v1.1) can be sufficiently supported by OPMProv using recursive views and SQL queries alone without any additional reasoning engine.
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In addition, you should add additional reasoning that support your contentions.
Third, the findings illustrated that prior knowledge, personal interpretation and additional reasoning influenced the interpretation of graph data.
These two subprocesses processes including identifying significant information and inferring also grouped under another main process, which is called Retrieving personal interpretation and additional reasoning.
It should be noted that the two strategies including identifying significant information and inferring are also affected by another main strategy called Retrieving Personal Interpretation and Additional Reasoning.
These processes involved Analyzing Non-Graphic InfofmatInstructionrucTranslatinglatinGraphicic and Non-Graphic Information into Written Discourse, Retrieving Personal Interpretation and Additional Reasoning, Evaluating Graph Comprehension, and Reformulating Graph Description into EFL Written Discourse.
While they were integrating graphic referents with pattern underlying the graph, they used their personal interpretations to identify significant pattern or information and additional reasoning to make inferences about difference between information presented in the graph.
The test takers were involved in different recursive process including interpreting graphic referents, then moved on to associate them with pattern underlying the graph and using prior knowledge, identifying significant pattern, and additional reasoning.
Although it was not required by GPT to make additional reasoning or personal interpretation, test takers deeply focused on the graphic information to comprehend why there were differences between categories of features or information they made in the graph.
Moreover, these differences in graphic knowledge made other test takers provide additional reasoning for the identified relations or categorize them through use of discreet values (e.g., higher, lower, equal, better, more, and less).
The findings explaining cognitive processes and its relation to subprocesses consist of Analyzing Non-Graphic Informathen (termterm was drawn from Yu et al. (2011), Translating Graphic and Non-Graphic Information into Written Discourse, Examining graph comprehension, Retrieving Personal Interpretation and Additional Reasoning, Reformulating Graph Description into EFL Written Discourse.
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