Attribute-Based Semantic Type Detection and Data Quality Assessment

TL;DR

This paper presents a novel semantic type detection method that leverages attribute labels and rule-based analysis to improve data quality assessment across diverse datasets, outperforming existing tools in accuracy.

Contribution

The research introduces an innovative semantic type classification system using attribute labels, rule-based analysis, and dictionaries, enhancing data quality detection and robustness.

Findings

Achieved 81 missing value detections in 922 attributes, outperforming YData Profiling.

Demonstrated superior accuracy and applicability over Sherlock in classifying semantic types.

Validated effectiveness across fifty datasets from the UCI repository.

Abstract

The reliance on data-driven decision-making across sectors highlights the critical need for high-quality data; despite advancements, data quality issues persist, significantly impacting business strategies and scientific research. Current data quality methods fail to leverage the semantic richness embedded in words inside attribute labels (or column names/headers in tables) across diverse datasets and domains, leaving a crucial gap in comprehensive data quality evaluation. This research addresses this gap by introducing an innovative methodology centered around Attribute-Based Semantic Type Detection and Data Quality Assessment. By leveraging semantic information within attribute labels, combined with rule-based analysis and comprehensive Formats and Abbreviations dictionaries, our approach introduces a practical semantic type classification system comprising approximately 23 types, including numerical non-negative, categorical, ID, names, strings, geographical, temporal, and complex formats like URLs, IP addresses, email, and binary values plus several numerical bounded types, such as age and percentage. A comparative analysis with Sherlock, a state-of-the-art Semantic Type Detection system, shows the advantages of our approach in terms of classification robustness and applicability to data quality assessment tasks. Our research focuses on well-known data quality issues and their corresponding data quality dimension violations, grounding our methodology in a robust academic framework. Detailed analysis of fifty distinct datasets from the UCI Machine Learning Repository showcases our method's proficiency in identifying potential data quality issues. Compared to established tools like YData Profiling, our method exhibits superior accuracy, detecting 81 missing values across 922 attributes where YData identified only one.