Use of Keyphrase Extraction Software for Creation of an AEC/FM Thesaurus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper describes a method used to collect terms needed for the development of a thesaurus in the roofing domain. This work is part of a larger effort to investigate the potential of thesauri as an aid in product modeling and as a tool for information management in model-based systems. Extractor, a software module that extracts keyphrases from documents, was used for collecting candidate thesaurus terms from Internet sources. The principal advantage of the Internet as a source of candidate terms is that it reflects the language that is actually used in communications concerning buildings and that it covers the widest range of different views on the domain. The advantage of using Extractor or similar software is that it allows processing huge text corpora available on the Internet while eliminating irrelevant terms. The methodology used was found to be highly useful, although it was not sufficient by itself for constructing a thesaurus for the architecture, engineering, construction and facilities management industries, as considerable human intervention was required. Some possibilities for customizing the software and for partially automating a thesaurus construction process are suggested.
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.
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 it