Retrieved Documents
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Retrieved Documents has 8 facts recorded in Dontopedia across 5 references, with 1 live disagreement.
Mostly:rdf:type(4), obtained via(1), is input to(1)
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Retrieved Documents has 8 facts recorded in Dontopedia across 5 references, with 1 live disagreement.
Mostly:rdf:type(4), obtained via(1), is input to(1)
isInputToresultOfusedAsOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
printsPrints(2)ex:example_usageex:print_statementreturnsReturns(2)ex:process_queryex:simulatebm25_retrievalassignsToAssigns to(1)ex:process_querycomputedFromComputed From(1)ex:pred_vectorisCalculatedFromIs Calculated From(1)ex:true_vectorresultsInResults in(1)ex:document_retrievalreturnsValueOfReturns Value of(1)ex:process_querystoresStores(1)ex:retrieved_documents_columnTimeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:beam/ca2653b8-c25f-4a54-bdfa-ff6ea71f5472true_vector = [doc in ground_truth_documents for doc in retrieved_documents] pred_vector = [True] * len(retrieved_documents) y_true.extend(true_vector) y_pred.extend(pred_vector) # Calculate precision and recall precision …
doc:beam/611f4162-5cb7-44ec-ba74-e4a6eac30c99[Turn 10605] Assistant: Collecting and structuring a dataset for evaluating the reformulated queries involves several steps. Here's a comprehensive guide to help you create a robust dataset and structure it effectively: ### Step 1: Define …
doc:beam/4ace5f5a-184d-4eae-9c13-25918a7725e6reformulated_query = reformulate_query(query, context) # Process the reformulated query (e.g., retrieve relevant documents) # This is a placeholder for the actual retrieval logic retrieved_documents = self.r…
doc:beam/4cc521bd-2791-4334-88dc-f5e3519e2d922. **Split the Dataset**: Divide the dataset into training and testing sets. 3. **Evaluate Precision and Recall**: Use precision and recall to evaluate the relevance of the retrieved documents. 4. **User Feedback**: Optionally, collect user…
doc:beam/f008f4ce-021d-4be6-b191-62e598ae1493dataset = pd.read_csv('queries_dataset.csv') # Split the dataset into training and testing sets train_data, test_data = train_test_split(dataset, test_size=0.2) # Train the RAG system (if needed) # ... # Evaluate the system on the test d…
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