Bibliographic record
Abstract
A focused crawler is an efficient tool used to traverse the Web to gather documents on a specific topic. It can be used to build domain‐specific Web search portals and online personalized search tools. Focused crawlers can only use information obtained from previously crawled pages to estimate the relevance of a newly seen URL. Therefore, good performance depends on powerful modeling of context as well as the quality of the current observations. To address this challenge, we propose capturing sequential patterns along paths leading to targets based on probabilistic models. We model the process of crawling by a walk along an underlying chain of hidden states, defined by hop distance from target pages, from which the actual topics of the documents are observed. When a new document is seen, prediction amounts to estimating the distance of this document from a target. Within this framework, we propose two probabilistic models for focused crawling, Maximum Entropy Markov Model (MEMM) and Linear‐chain Conditional Random Field (CRF). With MEMM, we exploit multiple overlapping features, such as anchor text, to represent useful context and form a chain of local classifier models. With CRF, a form of undirected graphical models, we focus on obtaining global optimal solutions along the sequences by taking advantage not only of text content, but also of linkage relations. We conclude with an experimental validation and comparison with focused crawling based on Best‐First Search (BFS), Hidden Markov Model (HMM), and Context‐graph Search (CGS).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".