Research on Entity Label Value Assignment Method in Knowledge Graph
Bibliographic record
Abstract
The lack of entity label values is one of the problems faced by the application of Knowledge Graph. The method of automatically assigning entity label values still has shortcomings, such as costing more resources during training, leading to inaccurate label value assignment because of lacking entity semantics. In this paper, oriented to domain-specific Knowledge Graph, based on the situation that the initial entity label values of all triples are completely unknown, an Entity Label Value Assignment Method (ELVAM) based on external resources and entropy is proposed. ELVAM first constructs a Relationship Triples Cluster according to the relationship type, and randomly extracts the triples data from each cluster to form a Relationship Triples Subset; then collects the extended semantic text of the entities in the subset from the external resources to obtain nouns. Information Entropy and Conditional Entropy of the nouns are calculated through Ontology Category Hierarchy Graph, so as to obtain the entity label value with moderate granularity. Finally, the Label Triples Pattern of each Relationship Triples Cluster is summarized, and the corresponding entity is assigned the label value according to the pattern. The experimental results verify the effectiveness of ELVAM in assigning entity label values in Knowledge Graph.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| 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".