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queries/queries_data-science-course.jsonl

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+{"id": "data-science-course_T1_58", "domain": "data-science-course", "type": "T1_entity", "query": "What is Null Detection?", "ground_truth": ["Null Detection", "CLEAN"], "concept_id": 58, "hop_depth": 0}
+{"id": "data-science-course_T1_13", "domain": "data-science-course", "type": "T1_entity", "query": "What is Dependent Variable?", "ground_truth": ["Dependent Variable", "FOUND"], "concept_id": 13, "hop_depth": 0}
+{"id": "data-science-course_T1_141", "domain": "data-science-course", "type": "T1_entity", "query": "What is Regression Coefficients?", "ground_truth": ["Regression Coefficients", "REGR"], "concept_id": 141, "hop_depth": 0}
+{"id": "data-science-course_T1_126", "domain": "data-science-course", "type": "T1_entity", "query": "What is Correlation?", "ground_truth": ["Correlation", "STATS"], "concept_id": 126, "hop_depth": 0}
+{"id": "data-science-course_T1_115", "domain": "data-science-course", "type": "T1_entity", "query": "What is Probability?", "ground_truth": ["Probability", "STATS"], "concept_id": 115, "hop_depth": 0}
+{"id": "data-science-course_T1_72", "domain": "data-science-course", "type": "T1_entity", "query": "What is Query Method?", "ground_truth": ["Query Method", "CLEAN"], "concept_id": 72, "hop_depth": 0}
+{"id": "data-science-course_T1_53", "domain": "data-science-course", "type": "T1_entity", "query": "What is Info Method?", "ground_truth": ["Info Method", "DSTRC"], "concept_id": 53, "hop_depth": 0}
+{"id": "data-science-course_T1_280", "domain": "data-science-course", "type": "T1_entity", "query": "What is Model Evaluation PyTorch?", "ground_truth": ["Model Evaluation PyTorch", "TORCH"], "concept_id": 280, "hop_depth": 0}
+{"id": "data-science-course_T1_45", "domain": "data-science-course", "type": "T1_entity", "query": "What is Row?", "ground_truth": ["Row", "DSTRC"], "concept_id": 45, "hop_depth": 0}
+{"id": "data-science-course_T1_217", "domain": "data-science-course", "type": "T1_entity", "query": "What is Feature Transformation?", "ground_truth": ["Feature Transformation", "ADVR"], "concept_id": 217, "hop_depth": 0}
+{"id": "data-science-course_T1_17", "domain": "data-science-course", "type": "T1_entity", "query": "What is Target Variable?", "ground_truth": ["Target Variable", "FOUND"], "concept_id": 17, "hop_depth": 0}
+{"id": "data-science-course_T1_16", "domain": "data-science-course", "type": "T1_entity", "query": "What is Feature?", "ground_truth": ["Feature", "FOUND"], "concept_id": 16, "hop_depth": 0}
+{"id": "data-science-course_T1_48", "domain": "data-science-course", "type": "T1_entity", "query": "What is Read CSV?", "ground_truth": ["Read CSV", "DSTRC"], "concept_id": 48, "hop_depth": 0}
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+{"id": "data-science-course_T3_2_270", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Python Programming to Automatic Differentiation?", "ground_truth": ["Automatic Differentiation", "Autograd", "PyTorch Library", "Python Libraries", "Import Statement", "Python Programming"], "concept_id": 270, "hop_depth": 5, "path_ids": [270, 269, 266, 34, 33, 2]}
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+{"id": "data-science-course_T3_2_162", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Python Programming to Adjusted R-Squared?", "ground_truth": ["Adjusted R-Squared", "Multiple Linear Regression", "Multiple Predictors", "Independent Variable", "Variables", "Python Programming"], "concept_id": 162, "hop_depth": 5, "path_ids": [162, 181, 182, 12, 5, 2]}
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+{"id": "data-science-course_T3_1_88", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Data Science to Figure Size?", "ground_truth": ["Figure Size", "Figure", "Matplotlib Library", "Data Visualization", "Data Science"], "concept_id": 88, "hop_depth": 4, "path_ids": [88, 78, 77, 76, 1]}
+{"id": "data-science-course_T3_1_229", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Data Science to Classification?", "ground_truth": ["Classification", "Supervised Learning", "Machine Learning", "Data Science"], "concept_id": 229, "hop_depth": 3, "path_ids": [229, 227, 226, 1]}
+{"id": "data-science-course_T3_1_75", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Data Science to Normalization?", "ground_truth": ["Normalization", "Feature Scaling", "Descriptive Statistics", "Data", "Data Science"], "concept_id": 75, "hop_depth": 4, "path_ids": [75, 74, 101, 4, 1]}
+{"id": "data-science-course_T3_1_109", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Data Science to Percentiles?", "ground_truth": ["Percentiles", "Quartiles", "Median", "Descriptive Statistics", "Data", "Data Science"], "concept_id": 109, "hop_depth": 5, "path_ids": [109, 108, 103, 101, 4, 1]}
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+{"id": "data-science-course_T3_2_68", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Python Programming to String Cleaning?", "ground_truth": ["String Cleaning", "DataFrame", "Pandas Library", "Python Libraries", "Import Statement", "Python Programming"], "concept_id": 68, "hop_depth": 5, "path_ids": [68, 41, 40, 34, 33, 2]}
+{"id": "data-science-course_T3_1_48", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Data Science to Read CSV?", "ground_truth": ["Read CSV", "CSV Files", "Data Loading", "Dataset", "Data", "Data Science"], "concept_id": 48, "hop_depth": 5, "path_ids": [48, 47, 46, 14, 4, 1]}
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+{"id": "data-science-course_T3_1_217", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Data Science to Feature Transformation?", "ground_truth": ["Feature Transformation", "Feature Engineering", "Feature", "Dataset", "Data", "Data Science"], "concept_id": 217, "hop_depth": 5, "path_ids": [217, 194, 16, 14, 4, 1]}
+{"id": "data-science-course_T3_2_67", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Python Programming to Data Validation?", "ground_truth": ["Data Validation", "DataFrame", "Pandas Library", "Python Libraries", "Import Statement", "Python Programming"], "concept_id": 67, "hop_depth": 5, "path_ids": [67, 41, 40, 34, 33, 2]}
+{"id": "data-science-course_T3_2_154", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Python Programming to Fit Method?", "ground_truth": ["Fit Method", "LinearRegression Class", "Scikit-learn Library", "Python Libraries", "Import Statement", "Python Programming"], "concept_id": 154, "hop_depth": 5, "path_ids": [154, 153, 152, 34, 33, 2]}
+{"id": "data-science-course_T3_1_278", "domain": "data-science-course", "type": "T3_path", "query": "What is the prerequisite chain from Data Science to Adam Optimizer?", "ground_truth": ["Adam Optimizer", "Optimizer", "PyTorch Library", "Neural Networks", "Machine Learning", "Data Science"], "concept_id": 278, "hop_depth": 5, "path_ids": [278, 276, 266, 246, 226, 1]}
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+{"id": "data-science-course_T4_PYENV", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all PYENV concepts in this knowledge graph", "ground_truth": ["Jupyter Notebooks", "Python Installation", "Package Management", "Pip", "Conda Environment", "Virtual Environment", "IDE Setup", "VS Code", "Notebook Cells", "Code Cell", "Markdown Cell", "Cell Execution", "Kernel", "Import Statement", "Python Libraries"], "taxonomy_id": "PYENV", "hop_depth": 0}
+{"id": "data-science-course_T4_BEST", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all BEST concepts in this knowledge graph", "ground_truth": ["Documentation", "Explainable AI", "Model Interpretability", "Feature Importance Analysis", "SHAP Values", "Model Documentation", "Reproducibility", "Random Seed", "Version Control", "Git", "Data Ethics"], "taxonomy_id": "BEST", "hop_depth": 0}
+{"id": "data-science-course_T4_DSTRC", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all DSTRC concepts in this knowledge graph", "ground_truth": ["Lists", "Dictionaries", "Tuples", "Arrays", "Pandas Library", "DataFrame", "Series", "Index", "Column", "Row", "Data Loading", "CSV Files", "Read CSV", "Data Inspection", "Head Method", "Tail Method", "Shape Attribute", "Info Method", "Describe Method", "Data Selection"], "taxonomy_id": "DSTRC", "hop_depth": 0}
+{"id": "data-science-course_T4_CLEAN", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all CLEAN concepts in this knowledge graph", "ground_truth": ["Missing Values", "NaN", "Null Detection", "Dropna Method", "Fillna Method", "Imputation", "Data Type Conversion", "Duplicate Detection", "Duplicate Removal", "Outliers", "Outlier Detection", "Data Validation", "String Cleaning", "Column Renaming", "Data Filtering", "Boolean Indexing", "Query Method", "Data Transformation", "Feature Scaling", "Normalization"], "taxonomy_id": "CLEAN", "hop_depth": 0}
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+{"id": "data-science-course_T4_STATS", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all STATS concepts in this knowledge graph", "ground_truth": ["Descriptive Statistics", "Mean", "Median", "Mode", "Range", "Variance", "Standard Deviation", "Quartiles", "Percentiles", "Interquartile Range", "Skewness", "Kurtosis", "Distribution", "Normal Distribution", "Probability", "Random Variables", "Expected Value", "Sample", "Population", "Sampling", "Central Limit Theorem", "Confidence Interval", "Hypothesis Testing", "P-Value", "Statistical Significance", "Correlation", "Covariance", "Pearson Correlation", "Spearman Correlation", "Correlation Matrix"], "taxonomy_id": "STATS", "hop_depth": 0}
+{"id": "data-science-course_T4_REGR", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all REGR concepts in this knowledge graph", "ground_truth": ["Regression Analysis", "Linear Regression", "Simple Linear Regression", "Regression Line", "Slope", "Intercept", "Least Squares Method", "Residuals", "Sum of Squared Errors", "Ordinary Least Squares", "Regression Coefficients", "Coefficient Interpretation", "Prediction", "Fitted Values", "Regression Equation", "Line of Best Fit", "Assumptions of Regression", "Linearity Assumption", "Homoscedasticity", "Independence Assumption", "Normality of Residuals", "Scikit-learn Library", "LinearRegression Class", "Fit Method", "Predict Method"], "taxonomy_id": "REGR", "hop_depth": 0}
+{"id": "data-science-course_T4_EVAL", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all EVAL concepts in this knowledge graph", "ground_truth": ["Model Performance", "Training Data", "Testing Data", "Train Test Split", "Validation Data", "R-Squared", "Adjusted R-Squared", "Mean Squared Error", "Root Mean Squared Error", "Mean Absolute Error", "Residual Analysis", "Residual Plot", "Overfitting", "Underfitting", "Bias", "Variance", "Bias-Variance Tradeoff", "Model Complexity", "Cross-Validation", "K-Fold Cross-Validation", "Leave One Out CV", "Holdout Method", "Model Selection", "Hyperparameters", "Model Comparison"], "taxonomy_id": "EVAL", "hop_depth": 0}
+{"id": "data-science-course_T4_ADVR", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all ADVR concepts in this knowledge graph", "ground_truth": ["Multiple Linear Regression", "Multiple Predictors", "Multicollinearity", "Variance Inflation Factor", "Feature Selection", "Forward Selection", "Backward Elimination", "Stepwise Selection", "Categorical Variables", "Dummy Variables", "One-Hot Encoding", "Interaction Terms", "Polynomial Features", "Feature Engineering", "Feature Importance", "Non-linear Regression", "Polynomial Regression", "Degree of Polynomial", "Curve Fitting", "Transformation", "Log Transformation", "Feature Transformation", "Model Flexibility", "Regularization", "Ridge Regression", "Lasso Regression", "Elastic Net", "Regularization Parameter", "Lambda Parameter", "Shrinkage"], "taxonomy_id": "ADVR", "hop_depth": 0}
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+{"id": "data-science-course_T4_NN", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all NN concepts in this knowledge graph", "ground_truth": ["Neural Networks", "Artificial Neuron", "Perceptron", "Activation Function", "Sigmoid Function", "ReLU Function", "Input Layer", "Hidden Layer", "Output Layer", "Weights", "Biases", "Forward Propagation", "Backpropagation", "Deep Learning", "Network Architecture", "Epochs", "Batch Size", "Mini-batch", "Stochastic Gradient", "Vanishing Gradient"], "taxonomy_id": "NN", "hop_depth": 0}
+{"id": "data-science-course_T4_TORCH", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all TORCH concepts in this knowledge graph", "ground_truth": ["PyTorch Library", "Tensors", "Tensor Operations", "Autograd", "Automatic Differentiation", "Computational Graph", "Neural Network Module", "Sequential Model", "Linear Layer", "Loss Functions PyTorch", "Optimizer", "SGD Optimizer", "Adam Optimizer", "Training Loop", "Model Evaluation PyTorch", "GPU Computing", "CUDA", "Model Saving", "Model Loading", "Transfer Learning"], "taxonomy_id": "TORCH", "hop_depth": 0}
+{"id": "data-science-course_T4_PROJ", "domain": "data-science-course", "type": "T4_aggregate", "query": "List all PROJ concepts in this knowledge graph", "ground_truth": ["Capstone Project", "End-to-End Pipeline", "Model Deployment", "Results Communication", "Data-Driven Decisions"], "taxonomy_id": "PROJ", "hop_depth": 0}
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+{"id": "data-science-course_T5_23_22", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Pip relate to Package Management?", "ground_truth": ["Pip", "Package Management"], "concept_id_a": 23, "concept_id_b": 22, "hop_depth": 1}
+{"id": "data-science-course_T5_66_65", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Outlier Detection relate to Outliers?", "ground_truth": ["Outlier Detection", "Outliers"], "concept_id_a": 66, "concept_id_b": 65, "hop_depth": 1}
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+{"id": "data-science-course_T5_103_101", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Median relate to Descriptive Statistics?", "ground_truth": ["Median", "Descriptive Statistics"], "concept_id_a": 103, "concept_id_b": 101, "hop_depth": 1}
+{"id": "data-science-course_T5_277_241", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does SGD Optimizer relate to Gradient Descent?", "ground_truth": ["SGD Optimizer", "Gradient Descent"], "concept_id_a": 277, "concept_id_b": 241, "hop_depth": 1}
+{"id": "data-science-course_T5_164_163", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Root Mean Squared Error relate to Mean Squared Error?", "ground_truth": ["Root Mean Squared Error", "Mean Squared Error"], "concept_id_a": 164, "concept_id_b": 163, "hop_depth": 1}
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+{"id": "data-science-course_T5_249_247", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Activation Function relate to Artificial Neuron?", "ground_truth": ["Activation Function", "Artificial Neuron"], "concept_id_a": 249, "concept_id_b": 247, "hop_depth": 1}
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+{"id": "data-science-course_T5_256_136", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Biases relate to Intercept?", "ground_truth": ["Biases", "Intercept"], "concept_id_a": 256, "concept_id_b": 136, "hop_depth": 1}
+{"id": "data-science-course_T5_173_132", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Model Complexity relate to Linear Regression?", "ground_truth": ["Model Complexity", "Linear Regression"], "concept_id_a": 173, "concept_id_b": 132, "hop_depth": 1}
+{"id": "data-science-course_T5_62_41", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Data Type Conversion relate to DataFrame?", "ground_truth": ["Data Type Conversion", "DataFrame"], "concept_id_a": 62, "concept_id_b": 41, "hop_depth": 1}
+{"id": "data-science-course_T5_75_74", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Normalization relate to Feature Scaling?", "ground_truth": ["Normalization", "Feature Scaling"], "concept_id_a": 75, "concept_id_b": 74, "hop_depth": 1}
+{"id": "data-science-course_T5_116_115", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Random Variables relate to Probability?", "ground_truth": ["Random Variables", "Probability"], "concept_id_a": 116, "concept_id_b": 115, "hop_depth": 1}
+{"id": "data-science-course_T5_85_108", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Box Plot relate to Quartiles?", "ground_truth": ["Box Plot", "Quartiles"], "concept_id_a": 85, "concept_id_b": 108, "hop_depth": 1}
+{"id": "data-science-course_T5_22_21", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Package Management relate to Python Installation?", "ground_truth": ["Package Management", "Python Installation"], "concept_id_a": 22, "concept_id_b": 21, "hop_depth": 1}
+{"id": "data-science-course_T5_205_197", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Matrix Operations relate to NumPy Array?", "ground_truth": ["Matrix Operations", "NumPy Array"], "concept_id_a": 205, "concept_id_b": 197, "hop_depth": 1}
+{"id": "data-science-course_T5_261_231", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Epochs relate to Training Process?", "ground_truth": ["Epochs", "Training Process"], "concept_id_a": 261, "concept_id_b": 231, "hop_depth": 1}
+{"id": "data-science-course_T5_191_190", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does One-Hot Encoding relate to Dummy Variables?", "ground_truth": ["One-Hot Encoding", "Dummy Variables"], "concept_id_a": 191, "concept_id_b": 190, "hop_depth": 1}
+{"id": "data-science-course_T5_24_22", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Conda Environment relate to Package Management?", "ground_truth": ["Conda Environment", "Package Management"], "concept_id_a": 24, "concept_id_b": 22, "hop_depth": 1}
+{"id": "data-science-course_T5_265_258", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Vanishing Gradient relate to Backpropagation?", "ground_truth": ["Vanishing Gradient", "Backpropagation"], "concept_id_a": 265, "concept_id_b": 258, "hop_depth": 1}
+{"id": "data-science-course_T5_121_114", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Central Limit Theorem relate to Normal Distribution?", "ground_truth": ["Central Limit Theorem", "Normal Distribution"], "concept_id_a": 121, "concept_id_b": 114, "hop_depth": 1}
+{"id": "data-science-course_T5_19_18", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Problem Definition relate to Data Science Workflow?", "ground_truth": ["Problem Definition", "Data Science Workflow"], "concept_id_a": 19, "concept_id_b": 18, "hop_depth": 1}
+{"id": "data-science-course_T5_208_205", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Transpose relate to Matrix Operations?", "ground_truth": ["Transpose", "Matrix Operations"], "concept_id_a": 208, "concept_id_b": 205, "hop_depth": 1}
+{"id": "data-science-course_T5_170_169", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Bias relate to Underfitting?", "ground_truth": ["Bias", "Underfitting"], "concept_id_a": 170, "concept_id_b": 169, "hop_depth": 1}
+{"id": "data-science-course_T5_178_172", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Model Selection relate to Bias-Variance Tradeoff?", "ground_truth": ["Model Selection", "Bias-Variance Tradeoff"], "concept_id_a": 178, "concept_id_b": 172, "hop_depth": 1}
+{"id": "data-science-course_T5_299_290", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Results Communication relate to Model Documentation?", "ground_truth": ["Results Communication", "Model Documentation"], "concept_id_a": 299, "concept_id_b": 290, "hop_depth": 1}
+{"id": "data-science-course_T5_187_185", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Backward Elimination relate to Feature Selection?", "ground_truth": ["Backward Elimination", "Feature Selection"], "concept_id_a": 187, "concept_id_b": 185, "hop_depth": 1}
+{"id": "data-science-course_T5_63_41", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Duplicate Detection relate to DataFrame?", "ground_truth": ["Duplicate Detection", "DataFrame"], "concept_id_a": 63, "concept_id_b": 41, "hop_depth": 1}
+{"id": "data-science-course_T5_180_161", "domain": "data-science-course", "type": "T5_cross_concept", "query": "How does Model Comparison relate to R-Squared?", "ground_truth": ["Model Comparison", "R-Squared"], "concept_id_a": 180, "concept_id_b": 161, "hop_depth": 1}