Cosine Similarity Indexing of Word Embeddings Using Knowledge Organization Systems
Bibliographic record
Abstract
This paper proposes a new technique for cosine similarity indexing in the era of large language models (LLMs). It investigates how knowledge organization systems (KOS) can be used to index the latent spaces which LLMs produce. A latent space is a multidimensional feature space used by a model to encode the context of data items. In the case of an LLM, a typical latent space is a word embedding, which gives every word a “position” in a multidimensional feature space, where the features are opaque, and not human-readable. This work asks: can indexing such latent spaces with KOSs help make LLMs more explainable? It builds on previous work in latent semantic indexing for information retrieval models to see if similar techniques can be used to bridge KOSs and LLMs. It also investigates how this method can be applied to improving the performance of multilingual information retrieval. A cross-lingual ontology (called Horapollo) is used to index two latent spaces containing Wikipedia articles written in English and Arabic. Then, the distance between equivalent articles in both spaces are taken, raising questions about the use of KOSs for multilingual and transdisciplinary information retrieval tasks in the era of semantic search.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".