Converting a Historical Architecture Encyclopedia into a Semantic Knowledge Base
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
Digitizing a historical document using ontologies and natural language processing techniques can transform it from arcane text to a useful knowledge base.The Handbook on Architecture (Handbuch der Architektur) was perhaps one of the most ambitious publishing projects ever. Like a 19thcentury Wikipedia, it attempted nothing less than a full account of all architectural knowledge available at the time, both past and present. It covers topics from Greek temples to contemporary hospitals and universities; from the design of individual construction elements such as window sills to large-scale town planning; from physics to design; from planning to construction. It also discusses architectural history and styles and a multitude of other topics, such as building conception, statics, and interior design.Not surprisingly, this project took longer than planned. The encyclopedia's first volume was partly published in 1880, and over the next 63 years more than 100 architects worked on what would become more than 140 individual publications with over 25,000 pages. One important insight of our work is that targeted text analysis support, already available today, can easily be integrated into common desktop tools to support users for their task at hand. While NLP techniques are far from perfect or comprehensive, they can already deliver knowledge discovery support that goes significantly beyond the currently used approach of full-text search and information retrieval.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it