Ontology Matching
12 papers with code • 0 benchmarks • 1 datasets
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Libraries
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AISECKG
Most implemented papers
Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
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.
Related terms search based on WordNet / Wiktionary and its application in Ontology Matching
A set of ontology matching algorithms (for finding correspondences between concepts) is based on a thesaurus that provides the source data for the semantic distance calculations.
Multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint
The data of the machine-readable Wiktionary are stored in a relational database, but with the help of D2R server the database is presented as an RDF store.
Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
Aligning Hyperbolic Representations: an Optimal Transport-based approach
ahoyosid/hyperbolic_alignment
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As a result of this formalism, we derive extensions to some existing Euclidean methods of OT-based domain adaptation to their hyperbolic counterparts.
BERTMap: A BERT-based Ontology Alignment System
Ontology alignment (a. k. a ontology matching (OM)) plays a critical role in knowledge integration.
Ontology Matching Through Absolute Orientation of Embedding Spaces
Ontology matching is a core task when creating interoperable and linked open datasets.
Truveta Mapper: A Zero-shot Ontology Alignment Framework
sadransh/ontology-matching-framework
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In this paper, a new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task.
Contextualized Structural Self-supervised Learning for Ontology Matching
Ontology matching (OM) entails the identification of semantic relationships between concepts within two or more knowledge graphs (KGs) and serves as a critical step in integrating KGs from various sources.
