CRAWLING THE CONSTRUCTION WEB – A MACHINE-LEARNING APPROACH WITHOUT NEGATIVE EXAMPLES
Bibliographic record
Abstract
Professionals and craftsmen in the construction sector make an intensive use of information in their decision-making processes but only make limited use of the abundant information that is potentially available to them, particularly on the web. Consequently, designs are impoverished, construction is defective, and innovation is delayed. To facilitate convivial access to focused information, we have developed a question-and-answer (Q-A) system (reported elsewhere). To support this system, we have developed an automated crawler that permits the establishment of a bank of relevant pages, adapted to the needs of this particular industry-user community. It is based on the machine-learning framework in which an intelligent decision unit is trained to distinguish between nontopic and informative pages. We show that standard approaches which use both positive and negative classes are sensitive to the noise in the negative class. We propose different techniques for learning without negative examples, since initially one only has limited, positive information labeled by human experts; they are evaluated. Our crawler that uses the positive examples-based learning (PEBL) framework is able to collect construction-oriented pages with high precision and discovery rate. It can also be used to build domain-specific collections of pages in different scientific or professional contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".