Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles
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.
Code (1)
Tasks
One-Shot LearningSimilar Papers 제목 키워드 기반
Beyond Activation Patterns: A Weight-Based Out-of-Context Explanation of Sparse Autoencoder Features
Sparse autoencoders (SAEs) have emerged as a powerful technique for decomposing language model representations into interpretable features. Current interpretation methods infer feature semantics from activation patterns,…
Language-centered Human Activity Recognition
Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is critical for applications in healthcare, safety, and industrial production. However, variations in activity patterns, device types, and se…
Activity RecognitionHuman Activity RecognitionAn argumentation system for reasoning with conflict-minimal paraconsistent ALC
The semantic web is an open and distributed environment in which it is hard to guarantee consistency of knowledge and information. Under the standard two-valued semantics everything is entailed if knowledge and informati…
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
Large language models (LLMs) excel at general-purpose tasks, yet adapting their responses to individual users remains challenging. Retrieval augmentation provides a lightweight alternative to fine-tuning by conditioning …
Canonical Functionalism: Defining Functional Structure without Observer-Relative Semantic Maps
Computational functionalism about consciousness is often criticized for relying on observer-relative interpretations of physical systems. This paper proposes a mathematical refinement of functionalism that avoids this pr…