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Ontology matching with semantic verification [PDF]
Web Semantics, 2009 ASMOV (Automated Semantic Matching of Ontologies with Verification) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies.
Jean-Mary YR +2 more
exaly +5 more sources
Foundational ontologies meet ontology matching: A survey
Semantic Web, 2021 Ontology matching is a research area aimed at finding ways to make different ontologies interoperable. Solutions to the problem have been proposed from different disciplines, including databases, natural language processing, and machine learning.
C. Trojahn +4 more
semanticscholar +5 more sources
The Evaluation of Ontology Matching versus Text [PDF]
Informatică economică, 2010 Lately, the ontologies have become more and more complex, and they are used in different domains. Some of the ontologies are domain independent; some are specific to a domain.
Andreea-Diana MIHIS
doaj +1 more source
OLaLa: Ontology Matching with Large Language Models [PDF]
International Conference on Knowledge Capture, 2023 Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible to incorporate this knowledge in a
S. Hertling, Heiko Paulheim
semanticscholar +1 more source
Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching [PDF]
International Workshop on the Semantic Web, 2022 Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques.
Yuan He +5 more
semanticscholar +1 more source
VersaMatch: Ontology Matching with Weak Supervision
Proceedings of the VLDB Endowment, 2023 Ontology matching is crucial to data integration for across-silo data sharing and has been mainly addressed with heuristic and machine learning (ML) methods.
Jonathan Fürst +2 more
semanticscholar +1 more source
Matching heterogeneous ontologies with adaptive evolutionary algorithm
Connection Science, 2022 An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology ...
Xingsi Xue +4 more
doaj +1 more source
Automatic background knowledge selection for matching biomedical ontologies. [PDF]
PLoS ONE, 2014 Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains ...
Daniel Faria +4 more
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Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm
Sensors, 2023 Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor
Yucheng Zhuang, Yikun Huang, Wenyu Liu
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Background knowledge in ontology matching: A survey
Semantic Web, 2022 Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated.
Jan Portisch, M. Hladik, Heiko Paulheim
semanticscholar +1 more source