{ "table-2-1": { "id": "table-2-1", "caption": "Table 2-1: Comparison of the different CBR approaches", "source_pdf_pages": [ 37, 38, 39, 40 ], "headers": [ "Criteria", "Textual CBR", "Conversational CBR", "Structural CBR" ], "integration": "Replace all interleaved table OCR on PDF37–39 and the prefix of PDF40 before section 2.5. Retain page provenance per row. Do not treat the columns as sequential prose.", "rows": [ { "pdf_page": 37, "cells": [ "Case Base", "A case is represented in free-text format. The cases may be structured according to headers, but the content of the headers is in free-text form. The case base is the collection of free texts that may be, for example, in electronic documents that are accessed on the Internet.", "Cases are represented by a list of question/answers. The list of questions and answers may vary from one case to another. There is no common data structure. The questions may be stored in a database but each question is not a separate field of the database.", "The case base is made out of records in a database. The records contain field whose values are standardised. Specific cases are recorded by assigning values to the attributes that have been defined. The initial case base may easily be extracted from an existing database." ] }, { "pdf_page": 37, "cells": [ "Query Case", "A query is represented by question in free-text form whose content is similar to the description part of the stored cases.", "A list of questions and answers following a dialog with the user.", "The query consists of a set of attribute values. These may be obtained by asking questions. In an academic advisement application, attributes are fields of interest and academic history." ] }, { "pdf_page": 37, "cells": [ "Results", "A list of documents that might be useful", "A list of possible actions attached to the cases that have been retrieved", "A list of database records that describe cases" ] }, { "pdf_page": 38, "cells": [ "Background Knowledge", "Dictionaries of similar terms like “High Distinction: competent” or “Microwave: Wireless” and information about the relationships between different words", "Ordering of questions, hierarchy of questions in a tree-like structure, possible answers for the questions that have been defined", "Domain model that defines the case structure. Rules to deduce values. Information on how to compute the similarity among attributes values" ] }, { "pdf_page": 38, "cells": [ "Initial Effort", "Defining terms, synonyms and stop-words. Analysing and setting up relationships between different words", "Storing dialogs within a case base, structuring the questions to organise the case base manually, and ordering the questions manually. This can be done by entering existing decision trees", "Defining the domain model. Importing the database or collecting cases according to the structure of the domain model. This can be done, for example, using an electronic questionnaire" ] }, { "pdf_page": 38, "cells": [ "Case Creation", "Writing new documents", "Adding new dialogs. Depending on the structure of the dialog, new questions must be entered.", "Adding a new record to the database. A questionnaire (forms) can be used in order to use the same vocabulary." ] }, { "pdf_page": 39, "cells": [ "Maintenance", "Maintaining the dictionary of controlled terms and sentences. Analysing user queries and results.", "Maintaining the list of questions and answers. Eliminating doubles, combining answers and questions. Reordering the questions by hand for the consultation.", "Maintaining the case base and the domain model. The CBR system must keep up with these modifications. It can automatically compute the order of the questions during consultation." ] }, { "pdf_page": 39, "cells": [ "Multilingual Case Bases", "Almost impossible. Must have different case bases.", "Difficult, no standardised thesaurus of terms.", "Easy to implement with a single case base." ] }, { "pdf_page": 39, "cells": [ "Advantages", "Existing documents can be used as cases. No initial investment is required for modelling the cases.", "The approach is intuitive and easy to understand. The system asks questions which are answered by the user and this dialog is stored in the case base.", "An existing database can be used to produce the initial case base. Creating additional cases can be done without any knowledge about how the CBR system will use the cases afterward." ] }, { "pdf_page": 40, "cells": [ "Drawbacks", "The user is not guided. The CBR system must cope with spelling mistakes. The quality of retrieval depends on the syntax and not on the real content of the cases. High costs for quality control of the CBR system.", "No explicit knowledge represented in the system for computing similarity. The interdependencies among different dialogs cannot be predicted. High maintenance costs.", "It may be difficult to create a predefined case structure. This can usually be achieved within most technical domains, but it becomes harder in softer domains. High investment costs." ] } ] }, "table-2-2": { "id": "table-2-2", "caption": "Table 2-2: PROFILE Relation", "source_pdf_pages": [ 57 ], "headers": [ "Fname", "Lname", "JobType", "Expertise" ], "integration": "After section 2.10.4.4 Fuzzy Relations heading and before 2.10.4.4.1 Relational Data Models. Replace corresponding loose OCR rows.", "rows": [ { "pdf_page": 57, "cells": [ "Bob", "McLedon", "Academic", "AI" ] }, { "pdf_page": 57, "cells": [ "Rob", "Mucker", "Industry", "Expert System" ] }, { "pdf_page": 57, "cells": [ "Nancy", "McCay", "Government", "Statistics" ] }, { "pdf_page": 57, "cells": [ "John", "Hunt", "Government", "Robotics" ] } ] }, "table-2-3": { "id": "table-2-3", "caption": "Table 2-3: a similarity matrix for expertise areas", "source_pdf_pages": [ 58 ], "headers": [ "", "Robotics", "Expert Systems", "AI", "Statistics" ], "integration": "After paragraph ending Minimal specialization and before section 2.10.4.4.2. Replace loose OCR matrix rows.", "rows": [ { "pdf_page": 58, "cells": [ "Robotics", "1.0", "0.6", "0.6", "0.2" ] }, { "pdf_page": 58, "cells": [ "Expert Systems", "0.6", "1.0", "0.9", "0.2" ] }, { "pdf_page": 58, "cells": [ "AI", "0.6", "0.9", "1.0", "0.2" ] }, { "pdf_page": 58, "cells": [ "Statistics", "0.2", "0.2", "0.2", "1.0" ] } ] }, "figure-4-5-1": { "id": "figure-4-5-1", "caption": "Figure 4-5: Correlation and Ranking table to show the efficiency of improvement algorithm over earlier versions — comparison between algorithm when performance is lower than difficulty", "headers": [ "Correlation: Trivial", "Correlation: Euclid", "Correlation: Improve", "Ranking: Trivial", "Ranking: Euclid", "Ranking: Improve" ], "rows": [ { "cells": [ "0.845", "0.655", "0.845", "3", "1", "3" ] }, { "cells": [ "1.000", "1.000", "1.000", "3", "3", "3" ] }, { "cells": [ "1.000", "1.000", "1.000", "3", "3", "3" ] }, { "cells": [ "1.000", "1.000", "1.000", "3", "3", "3" ] }, { "cells": [ "0.964", "0.964", "1.000", "2", "2", "3" ] }, { "cells": [ "0.964", "0.964", "0.964", "3", "3", "3" ] }, { "cells": [ "0.802", "0.926", "0.964", "1", "2", "3" ] }, { "cells": [ "1.000", "1.000", "1.000", "3", "3", "3" ] }, { "cells": [ "7.574", "7.508", "7.772", "21.000", "20.000", "24.000" ] } ] }, "figure-4-5-2": { "id": "figure-4-5-2", "caption": "Figure 4-5: Correlation and Ranking table to show the efficiency of improvement algorithm over earlier versions — comparison between algorithm when prerequisites are not yet fulfilled", "headers": [ "Correlation: Trivial", "Correlation: Euclid", "Correlation: Improve", "Ranking: Trivial", "Ranking: Euclid", "Ranking: Improve" ], "rows": [ { "cells": [ "0.886", "0.756", "0.802", "3", "1", "2" ] }, { "cells": [ "0.756", "0.886", "0.964", "1", "2", "3" ] }, { "cells": [ "1.000", "1.000", "1.000", "3", "3", "3" ] }, { "cells": [ "0.655", "0.655", "0.802", "2", "2", "3" ] }, { "cells": [ "0.802", "0.655", "1.000", "2", "1", "3" ] }, { "cells": [ "0.655", "0.655", "0.964", "2", "2", "3" ] } ] }, "table-4-1": { "id": "table-4-1", "source_pdf_pages": [ 142 ], "printed_page": 132, "caption": "Table 4-1: Process Types", "headers": [ "", "", "System Development", "System Use" ], "rows": [ { "cells": [ "Managerial Processes", "", "Goal Definition\nAwareness Creation\nCBR-Tool Selection", "Progress Verification and Controlling" ], "merge_cells": [ [ 0, 1 ] ] }, { "cells": [ "Organisational Processes", "", "Project Team Selection\nInitial Domain Selection\nProject Team Training\nKnowledge Acquisition Process Development\nUtilisation Process Development", "End-User Training\nContinuous Knowledge Acquisition\nUtilisation Process" ], "merge_cells": [ [ 0, 1 ] ] }, { "cells": [ "Technical Processes", "General IT-System Related", "System Specification\nSystem Implementation\nSystem Integration\nSystem Verification", "Continuous System Maintenance" ] }, { "cells": [ "", "Knowledge Repository Related", "Initial Knowledge Acquisition\nCore Knowledge Acquisition", "Continuous Knowledge Acquisition and Maintenance" ] } ], "vertical_merge": { "column": 0, "rows": [ 2, 3 ] }, "integration": "Replace interleaved OCR table after section4.8.2.1 explanatory paragraph on PDF142. Retain source grammar and distinguish wrapped labels from separate entries.", "note": "Blank first two column headings are intentional, as in source; two Technical Processes subtypes share the first-column group." }, "table-D-1": { "id": "table-D-1", "pdf_page": 193, "printed_page": 184, "caption": "Table D-1: comparison between algorithms when performance is lower than difficulty", "headers": [ "Test case", "Expected Result", "Trivial IR", "Euclidean Distance", "Improvement" ], "rows": [ { "cells": [ "0, 2, 0, 3, 1, 2, 3", "NS7, NM5, O2", "NS7, OM3, O2", "O2, OM3, NS7", "NS7, OM3, O2" ] }, { "cells": [ "0, 3, 0, 1, 2, 1, 1", "OM3, NS7, D1", "OM3, NS7, D1", "OM3, NS7, D1", "OM3, NS7, D1" ] }, { "cells": [ "3, 2, 0, 0, 1, 2, 2", "D1, O2, OM3", "D1, O2, OM3", "D1, O2, OM3", "D1, O2, OM3" ] }, { "cells": [ "0, 3, 1, 2, 0, 1, 3", "OM3, NS7, D1", "OM3, NS7, D1", "OM3, NS7, D1", "OM3, NS7, D1" ] }, { "cells": [ "0, 1, 0, 3, 2, 1, 3", "NS7, OM3, D1", "NS7, OM3 (2)", "NS7, OM3 (2)", "NS7, OM3, D1" ] }, { "cells": [ "0, 1, 3, 0, 2, 2, 3", "OM3, NM5, S8", "OM3, NM5, NS7", "OM3, NM5, NS7", "OM3, NM5, NS7" ] }, { "cells": [ "0, 0, 1, 2, 3, 2, 1", "NS7, NM5, OM3", "NM5, S8, NS7", "NM5, NS7, S8", "NS7, NM5, S8" ] }, { "cells": [ "1, 2, 0, 0, 3, 1, 2", "NS7, OM3, D1", "NS7, OM3, D1", "NS7, OM3, D1", "NS7, OM3, D1" ] } ], "note": "Original course order and '(2)' marker preserved, without interpreting marker meaning or reconciling with prose examples." }, "table-D-2": { "id": "table-D-2", "pdf_page": 193, "printed_page": 184, "caption": "Table D-2: comparison between algorithms when prerequisites are not yet fulfilled", "headers": [ "Test case", "Expected Result", "Trivial IR", "Euclidean", "Improvement" ], "rows": [ { "cells": [ "0, 2, 0, 3, 1, 3, 3", "N6, NS7, NM5", "N6, OM3, NS7", "OM3, N6, NS7", "NS7, OM3, N6" ] }, { "cells": [ "0, 3, 0, 1, 2, 3, 1", "OM3, NS7, NM5", "N6, OM3, NS7", "OM3, N6, NS7", "NS7, OM3, NM5" ] }, { "cells": [ "3, 2, 0, 0, 1, 3, 2", "D1, OM3, NS7", "D1, OM3, NS7", "D1, OM3, NS7", "D1, OM3, NS7" ] }, { "cells": [ "0, 3, 1, 2, 0, 3, 3", "OM3, N6, NM5", "M4, OM3, D1", "M4, OM3, D1", "OM3, M4, NS7" ] }, { "cells": [ "0, 1, 0, 3, 2, 3, 3", "N6, S8, O2", "N6, OM3, NS7", "OM3, N6, NS7", "N6, S8, O2" ] }, { "cells": [ "0, 1, 3, 0, 2, 3, 3", "M4, S8, O2", "OM3, M4, NS7", "OM3, M4, NM5", "M4, S8, D1" ] }, { "cells": [ "0, 0, 1, 2, 3, 3, 1", "S8, N6, M4", "NM5, NS7, D1", "NM5, NS7, D1", "S8, N6, D1" ] }, { "cells": [ "1, 2, 0, 0, 3, 3, 2", "S8, O2, D1", "NS7, OM3, NM5", "NS7, OM3, NM5", "S8, O2, D1" ] } ], "note": "Header 'Euclidean' retained as printed. No code reexecution or quantitative validation implied." }, "appendix-E-advice-correlation": { "id": "appendix-E-advice-correlation", "pdf_page": 196, "header_pdf_page": 195, "printed_page": 187, "caption": "Correlation of Interests (INT) and Difference of Means — source screen advice table", "headers": [ "Description", "D1", "O2", "OM3", "M4", "NM5", "N6", "NS7", "S8" ], "rows": [ { "cells": [ "Prequisite", "1", "1", "0", "1", "0", "1", "0", "1" ] }, { "cells": [ "Difficulty", "1", "2", "1", "3", "2", "3", "1", "2" ] }, { "cells": [ "Count", "3", "2", "6", "0", "4", "5", "9", "1" ] }, { "cells": [ "Ranked Percentage", "88.209", "98.413", "99.206", "0.000", "93.311", "99.206", "99.206", "83.107" ] }, { "cells": [ "Weighted Sum according to Counts", "20.000", "24.000", "64.000", "0.000", "41.333", "52.000", "94.667", "4.000" ] } ], "note": "Editorial caption identifies source screen table. Original misspelling Prequisite retained." }, "appendix-E-advice-distance": { "id": "appendix-E-advice-distance", "pdf_page": 196, "printed_page": 187, "caption": "Distance of 2 vectors — source screen advice table", "headers": [ "Description", "D1", "O2", "OM3", "M4", "NM5", "N6", "NS7", "S8" ], "rows": [ { "cells": [ "Prequisite", "1", "1", "0", "1", "0", "1", "0", "1" ] }, { "cells": [ "Difficulty", "1", "2", "1", "3", "2", "3", "1", "2" ] }, { "cells": [ "Count", "2", "3", "5", "0", "4", "5", "9", "2" ] }, { "cells": [ "Ranked Percentage", "78.125", "87.500", "93.750", "0.000", "81.250", "93.750", "93.750", "75.000" ] }, { "cells": [ "Weighted Sum according to Counts", "13.333", "30.667", "54.667", "0.000", "44.000", "54.667", "92.000", "10.667" ] } ] }, "appendix-E-advice-trivial": { "id": "appendix-E-advice-trivial", "pdf_page": 196, "printed_page": 187, "caption": "Trivial IR algorithm — source screen advice table", "headers": [ "Description", "D1", "O2", "OM3", "M4", "NM5", "N6", "NS7", "S8" ], "rows": [ { "cells": [ "Prequisite", "1", "1", "0", "1", "0", "1", "0", "1" ] }, { "cells": [ "Difficulty", "1", "2", "1", "3", "2", "3", "1", "2" ] }, { "cells": [ "Count", "2", "3", "5", "0", "5", "5", "9", "1" ] }, { "cells": [ "Ranked Percentage", "88.454", "93.819", "97.991", "0.000", "90.211", "97.991", "97.991", "87.500" ] }, { "cells": [ "Weighted Sum according to Counts", "13.333", "30.667", "54.667", "0.000", "48.000", "54.667", "92.000", "6.667" ] } ] }, "appendix-E-problem-initial": { "id": "appendix-E-problem-initial", "pdf_page": 194, "caption": "Problem (using Knowledge 47)", "headers": [ "DSP", "OPTIC", "MWAVE", "NTWRK", "SATEL", "PRFM", "CHRT", "problem size" ], "rows": [ { "cells": [ "0", "2", "0", "3", "1", "3", "3", "5.657" ] } ] }, "appendix-E-ranked-correlation": { "id": "appendix-E-ranked-correlation", "pdf_page": 194, "caption": "Correlation of Interests (INT) and Difference of Means", "headers": [ "DSP", "OPTIC", "MWAVE", "NTWRK", "SATEL", "PRFM", "CHRT", "case", "percent", "D1", "O2", "OM3", "M4", "NM5", "N6", "NS7", "[rightmost header clipped]" ], "rows": [ { "cells": [ "0", "2", "0", "3", "1", "2", "2", "14", "99.206", "0", "0", "1", "0", "0", "1", "1", null ], "source_pdf_page": 194, "source_row": 1 }, { "cells": [ "0", "2", "0", "3", "1", "1", "3", "13", "98.413", "0", "1", "0", "0", "0", "1", "1", null ], "source_pdf_page": 194, "source_row": 2 }, { "cells": [ "0", "2", "1", "3", "0", "3", "1", "16", "93.311", "0", "0", "1", "0", "1", "0", "1", null ], "source_pdf_page": 194, "source_row": 3 }, { "cells": [ "1", "2", "0", "3", "0", "1", "3", "26", "93.311", "0", "0", "0", "0", "1", "1", "1", null ], "source_pdf_page": 194, "source_row": 4 }, { "cells": [ "0", "1", "0", "3", "2", "2", "1", "8", "92.914", "0", "0", "1", "0", "1", "0", "1", null ], "source_pdf_page": 194, "source_row": 5 }, { "cells": [ "0", "3", "0", "2", "1", "1", "2", "18", "92.914", "0", "1", "1", "0", "0", "0", "1", null ], "source_pdf_page": 194, "source_row": 6 }, { "cells": [ "1", "3", "0", "2", "0", "3", "1", "29", "88.209", "1", "0", "1", "0", "0", "0", "1", null ], "source_pdf_page": 194, "source_row": 7 }, { "cells": [ "1", "3", "0", "2", "0", "1", "1", "28", "86.621", "1", "0", "1", "0", "0", "0", "1", null ], "source_pdf_page": 194, "source_row": 8 }, { "cells": [ "2", "1", "0", "3", "0", "3", "2", "36", "84.297", "1", "0", "0", "0", "1", "1", "0", null ], "source_pdf_page": 194, "source_row": 9 }, { "cells": [ "0", "1", "0", "2", "3", "1", "3", "7", "83.107", "0", "0", "0", "0", "0", "1", "1", "1" ], "source_pdf_page": 194, "source_row": 10 } ], "note": "Rows1–9 retain only partial rightmost glyphs; row10's narrow1 is fully visible. 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