MTab: Matching Tabular Data to Knowledge Graph using Probability Models
This work addresses the challenge of linking tabular data to knowledge graphs for data integration and semantic web applications, but it appears incremental as it builds on existing methods for a specific competition.
The paper tackles the problem of matching tabular data to a knowledge graph by introducing MTab, a system that combines voting algorithms and probability models, achieving promising performance on the SemTab 2019 challenge across three matching tasks.
This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019). MTab combines the voting algorithm and the probability models to solve critical problems of the matching tasks. Results on SemTab 2019 show that MTab obtains promising performance for the three matching tasks.