Ontology Driven Document Identification in Semantic Web
Bibliographic record
Abstract
The concept of Semantic Web (Berners, 2001) introduces a new form of knowledge representation – an ontology. An ontology is a partially ordered set of words and concepts of a specific domain, and allows for defining different kinds of relationships existing among concepts. Such approach promises formation of an environment where information is easily accessible and understandable for any system, application and/or human. Hierarchy of concepts (Yager, 2000) is a different and very interesting form of knowledge representation. A graph-like structure of the hierarchy provides a user with a suitable tool for identifying variety of different associations among concepts. These associations express user’s perceptions of relations among concepts, and lead to representing definitions of concepts in a human-like way. The Internet becomes an overwhelming repository of documents. This enormous storage of information will be effectively used when users will be equipped with systems capable of finding related documents quickly and correctly. The proposed work addresses that issue. It offers an approach that combines a hierarchy of concepts and ontology for the task of identifying web documents in the environment of the Semantic Web. A user provides a simple query in the form a hierarchy that only partially “describes” documents (s)he wants to retrieve from the web. The hierarchy is treated as a “seed” representing user’s initial knowledge about concepts covered by required documents. Ontologies are treated as supplementary knowledge bases. They are used to instantiate the hierarchy with concrete information, as well as to enhance it with new concepts initially unknown to the user. The proposed approach is used to design a prototype system for document identification in the web environment. The description of the system and the results of preliminary experiments are presented.
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.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".