Papers › Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via...
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring
Shervin Mehryar, Remzi Celebi
In this paper we investigate automated annotation of tabular data using semantic technologies in combination with neural network embedding. Specifically, we propose an anchoring model in which property and cell types from the data embedding space are aligned with ontology relation and entity types. We show that by combining the power of symbolic reasoning, neural embeddings, and loss function design, a significant performance improvement as high as 86% for column property, 82% for column type, and 87% for column qualifier annotations can be achieved based on DBpedia and Wikidata table extractions.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Column Type Annotation | WDC SOTAB V2 | MUT2KG | Micro F1 | 32.01 | #5 of 5 | Archive leaderboard | report |
| Columns Property Annotation | WDC SOTAB V2 | MUT2KG | Micro F1 | 79.35 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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