MTab: Matching Tabular Data to Knowledge Graph using Probability Models

10/01/2019

∙

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.

READ FULL TEXT

MTab: Matching Tabular Data to Knowledge Graph using Probability Models

AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Exploiting Network Structures to Improve Semantic Representation for the Financial Domain

Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System

Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History

Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning

The Wind in Our Sails: Developing a Reusable and Maintainable Dutch Maritime History Knowledge Graph

MTab: Matching Tabular Data to Knowledge Graph using Probability Models

Related Research

AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Exploiting Network Structures to Improve Semantic Representation for the Financial Domain

Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System

Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History

Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning

The Wind in Our Sails: Developing a Reusable and Maintainable Dutch Maritime History Knowledge Graph