A Semantic Metadata Enrichment Software Ecosystem Based on Topic Metadata Enrichment
Bibliographic record
Abstract
As existing computer search engines struggle to understand the meaning of natural language, semantically enriched metadata may improve interest-based search engine capabilities and user satisfaction. This paper presents an enhanced version of the ecosystem focusing on semantic topic metadata detection and enrichment. It is based on a previous paper, a semantic metadata enrichment software ecosystem (SMESE). Through text analysis approaches for topic detection and metadata enrichment this paper propose an algorithm to enhance search engines capabilities and consequently help users finding content according to their interests. It presents the design, implementation and evaluation of SATD (Scalable Annotation-based Topic Detection) model and algorithm using metadata from the web, linked open data, concordance rules, and bibliographic record authorities. It includes a prototype of a semantic engine using keyword extraction, classification and concept extraction that allows generating semantic topics by text, and multimedia document analysis using the proposed SATD model and algorithm. The performance of the proposed ecosystem is evaluated using a number of prototype simulations by comparing them to existing enriched metadata techniques (e.g., AlchemyAPI, DBpedia, Wikimeta, Bitext, AIDA, TextRazor). It was noted that SATD algorithm supports more attributes than other algorithms. The results show that the enhanced platform and its algorithm enable greater understanding of documents related to user interests.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".