Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology mapping: the state of the art
The Knowledge Engineering Review
ACM SIGMOD Record
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
Falcon-AO: A practical ontology matching system
Web Semantics: Science, Services and Agents on the World Wide Web
Combining RDF Vocabularies for Expert Finding
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
idMesh: graph-based disambiguation of linked data
Proceedings of the 18th international conference on World wide web
A declarative framework for semantic link discovery over relational data
Proceedings of the 18th international conference on World wide web
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
Discovering and Maintaining Links on the Web of Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
An adaptive ontology mapping approach with neural network based constraint satisfaction
Web Semantics: Science, Services and Agents on the World Wide Web
UFOme: An ontology mapping system with strategy prediction capabilities
Data & Knowledge Engineering
Disambiguating identity web references using Web 2.0 data and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
One size does not fit all: customizing ontology alignment using user feedback
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Ontology alignment for linked open data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Finding association rules in semantic web data
Knowledge-Based Systems
LogMap: logic-based and scalable ontology matching
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Markov network based ontology matching
Journal of Computer and System Sciences
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
OMEN: a probabilistic ontology mapping tool
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Ontology Matching: State of the Art and Future Challenges
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 0.00 |
In recent years, the Web has evolved from a global information space of linked documents to a space where data are linked as well. The Linking Open Data (LOD) project has enabled a large number of semantic datasets to be published on the Web. Due to the open and distributed nature of the Web, both the schema (ontology classes and properties) and instances of the published datasets may have heterogeneity problems. In this context, the matching of entities from different datasets is important for the integration of information from different data sources. Recently, much work has been conducted on ontology matching to resolve the schema heterogeneity problem in the semantic datasets. However, there is no unified framework for matching both schema entities and instances. This paper presents a unified matching approach to finding equivalent entities in ontologies and LOD datasets on the Web. The approach first combines multiple lexical matching strategies using a novel voting-based aggregation method; then it utilizes the structural information and the already found correspondences to discover additional ones. We evaluated our approach using datasets from both OAEI and LOD. The results show that the voting-based aggregation method provides highly accurate matching results, and that the structural propagation procedure effectively improves the recall of the results.