Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles

TL;DR

Tab2KG introduces a lightweight, profile-based method for automatically interpreting unseen tabular data by transforming it into semantic graphs, enhancing robustness and explainability in data analytics.

Contribution

It presents a novel one-shot learning approach using semantic profiles that does not require instance lookup, unlike existing methods.

Findings

Outperforms state-of-the-art semantic interpretation methods

Effective on real-world datasets from various domains

Enables semantic interpretation without instance lookup

Abstract

Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG - a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation baselines.