Enhancing Search Engine Quality Using Concept-based Text Retrieval
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
Most of the common techniques in text retrieval are based on the statistical analysis of a term either as a word or a phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying representation should indicate terms that capture the semantics of text. In this case, the representation can capture terms that present the concepts of the sentence, which leads to discover the topic of the document. A new concept-based representation, called Conceptual Ontological Graph (COG), where a concept can be either a word or a phrase and totally dependent on the sentence semantics, is introduced. The aim of the proposed representation is to extract the most important terms in a sentence and a document with respect to the meaning of the text. The COG representation analyzes each term at both the sentence and the document levels. This is different from the classical approach of analyzing terms at the document level. First, the proposed representation denotes the terms which contribute to the sentence semantics. Then, each term is chosen based on its position within the COG representation. Lastly, the selected terms are associated to their documents as features for the purpose of indexing before text retrieval. The COG representation can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the key concepts that represent the sentence meaning. Large sets of experiments using the proposed COG representation on different datasets in text retrieval are conducted. Experimental results demonstrate the substantial enhancement of the text retrieval quality using the COG representation over the traditional techniques. The evaluation of results relies on two quality measures, the bpref and P(10). Both the quality measures improved when the newly developed COG representation is used to enhance the quality of the text retrieval results.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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