Cosine Similarity Indexing of Word Embeddings Using Knowledge Organization Systems
Why this work is in the frame
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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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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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