Synthesis of Concepts and Applications of Information Intelligent Systems and Knowledge Bases in Computer Science: A Systematic Literature Review
Bibliographic record
Abstract
In the era of technological advancement, the ubiquity of artificial intelligence necessitates a comprehensive understanding of information intelligent systems and knowledge bases in computer science.This systematic literature review addresses this essential need.The primary aim is to analyze and synthesize key aspects of practical applications of intelligent systems and the creation of knowledge bases in computer science.The research employs a combination of system analysis and analytical study methodologies.It involves a thorough literature search, encompassing various studies that focus on the principles of building information intelligent systems and knowledge bases within the field of informatics.The process includes critical examination and synthesis of data from selected studies, aiming to draw comprehensive insights.The research identifies and discusses various aspects of information intelligent systems, including their practical applications and the interaction with knowledge bases.Key findings include a detailed classification of knowledge bases according to their complexity and the role of artificial intelligence in these systems.The synthesis reveals how these systems fulfill diverse user queries through question-andanswer frameworks, highlighting their significance in modern informatics.The study underscores the importance of advanced knowledge processing technologies in computer science.The findings suggest that the effective development and implementation of information intelligent systems and knowledge bases are pivotal for modern education and various professional fields.This systematic review provides a foundation for future advancements in artificial intelligence applications, offering valuable insights for both academic and practical applications in the realm of computer science.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".