Hybrid Contextual Ontology-Fuzzy Logic and LSTM Model for Efficient Web Crawler Prediction and Traffic Optimization
Bibliographic record
Abstract
Efficient management of web crawlers and prediction of their behavior remain key challenges in the domain of intelligent information retrieval, particularly when multiple crawlers operate simultaneously in dynamic environments.This research presents a hybrid framework that integrates long short-term memory (LSTM) networks, fuzzy logic, and contextual ontology to enhance the accuracy and efficiency of web crawling and traffic optimization.The LSTM component is responsible for identifying temporal patterns in historical crawling behavior and predicting future crawler actions, while fuzzy logic deals with uncertain or imprecise web data, enabling smoother decision-making processes.To enrich the semantic understanding of web content and improve context-based data extraction, a contextual ontology is employed, allowing for intelligent interpretation and classification of retrieved web data.The proposed model was evaluated using two largescale benchmark datasets: Common Crawl (250 GB) and ClueWeb09 (1 TB).These datasets were chosen for their diversity and representation of real-world web structures and content.Experimental results demonstrate that the proposed system outperforms conventional approaches, achieving an 18.3% improvement in prediction accuracy and a 15.7% reduction in network traffic, compared to baseline LSTM and rule-based models.These results confirm the model's capability to reduce redundant data retrieval, avoid crawler overlaps, and enhance resource efficiency.This study highlights the potential of combining machine learning with semantic technologies to improve web crawling in complex environments.The proposed hybrid system offers a scalable, intelligent solution for high-performance information retrieval and efficient network traffic management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".