e-Document Standards as Background Knowledge in Context-Based Ontology Matching

2015 ◽  
pp. 806-816
Author(s):  
Audun Vennesland
Keyword(s):  
Background Knowledge ◽  
Ontology Matching

Review of ontology matching with background knowledge

2016 ◽  
Author(s):  
Inne Gartina Husein ◽  
Saiful Akbar ◽  
Benhard Sitohang ◽  
Fazat Nur Azizah
Keyword(s):  
Background Knowledge ◽  
Ontology Matching

scholarly journals Building an effective and efficient background knowledge resource to enhance ontology matching

2018 ◽  
Vol 51 ◽  
pp. 51-68 ◽  
Author(s):  
Amina Annane ◽  
Zohra Bellahsene ◽  
Faiçal Azouaou ◽  
Clement Jonquet
Keyword(s):  
Background Knowledge ◽  
Ontology Matching ◽  
Knowledge Resource

scholarly journals Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 487
Author(s):  
Sohaib Al-Yadumi ◽  
Wei-Wei Goh ◽  
Ee-Xion Tan ◽  
Noor Zaman Jhanjhi ◽  
Patrice Boursier
Keyword(s):  
State Of The Art ◽  
Model Performance ◽  
Background Knowledge ◽  
Ontology Matching ◽  
Semantic Heterogeneity ◽  
Ontology Alignment ◽  
Selection Algorithm ◽  
Confidence Measure ◽  
Proposed Model ◽  
Ontology Alignment Evaluation Initiative

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.

scholarly journals Building an Effective and Efficient Background Knowledge Resource to Enhance Ontology Matching

2018 ◽  
Author(s):  
Amina Annane ◽  
Zohra Bellahsene ◽  
Faical Azouaou ◽  
Clement Jonquet
Keyword(s):  
Background Knowledge ◽  
Ontology Matching ◽  
Knowledge Resource
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