Ontology guided data linkage framework for discovering meaningful data facts

Authors:
Mohammed Gollapalli;Xue Li;Ian Wood;Guido Governatori
Affiliations:
School of Information Technology & Electricial Engineering, University of Queensland, Brisbane, Australia;School of Information Technology & Electricial Engineering, University of Queensland, Brisbane, Australia;School of Mathematics & Physics, University of Queensland, Brisbane, Australia;Queensland Research Laboratory, National ICT Australia (NICTA), Brisbane, Australia
Venue:
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Year:
2011

Citing 8
Cited 1

CORDS: automatic discovery of correlations and soft functional dependencies

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
From databases to dataspaces: a new abstraction for information management

ACM SIGMOD Record
Nested mappings: schema mapping reloaded

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Ontology Matching

Ontology Matching
Schema merging and mapping creation for relational sources

EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Schema mapping verification: the spicy way

EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Discovering topical structures of databases

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Top-k generation of integrated schemas based on directed and weighted correspondences

Proceedings of the 2009 ACM SIGMOD International Conference on Management of data

Automated discovery of multi-faceted ontologies for accurate query answering and future semantic reasoning

Data & Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Making sensible queries on databases collected from different organizations presents a challenging task for linking semantic equivalent data facts. Current techniques primarily focused on performing pair-wise attribute matching and paid little attention towards discovering probabilistic structural dependencies by exploiting the ontological domain knowledge of tables, attributes and tuples to construct hierarchical cluster mapping trees. In this paper, we present Ontology Guided Data Linkage (OGDL) framework for self-organizing heterogeneous data sources into homogeneous ontological clusters through multi-faceted classification. Through the evaluation on real-world data, we demonstrate the robustness and accuracy of our system.