knowledge-base-6

Ontology Matching

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in ontology-matching-6

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Libraries

i

Use these libraries to find ontology-matching-6 models and implementations

KRR-Oxford/DeepOnto

2 papers 153

Datasets

AISECKG

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No subtasks available.

Most implemented papers

Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

Yuan He, Jiaoyan Chen, Hang Dong, Ernesto Jiménez-Ruiz, Ian Horrocks, A. Hadian•Thu May 05 2022

Five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS are introduced and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems.

42

Content

componavt/wikokit

2 papers 68

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Related terms search based on WordNet / Wiktionary and its application in Ontology Matching

A. A. Krizhanovsky, F. Lin•Sun Jul 12 2009

The experiment shows that the proposed method is capable in principle of calculating a semantic distance between pair of words in any language presented in Russian Wiktionary, and compared to WordNet based algorithms.

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Multilingual Ontology Matching based on Wiktionary Data Accessible via SPARQL Endpoint

F. Lin, Andrew Krizhanovsky•Wed Aug 31 2011

In the case study, the problem entity is a task of multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint, and Ontology matching results obtained usingWiktionary were compared with results based on Google Translate API.

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Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings

Ernesto Jiménez-Ruiz, Asan Agibetov, M. Samwald, V. Cross•Wed May 30 2018

An approach that combines a lexical index, a neural embedding model and locality modules to effectively divide an input ontology matching task into smaller and more tractable matching (sub)tasks is presented.

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Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules

Jiaoyan Chen, Ernesto Jiménez-Ruiz, Asan Agibetov, M. Samwald, V. Cross•Sun Jan 19 2020

An approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks is presented.

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Aligning Hyperbolic Representations: an Optimal Transport-based approach

Andrés Hoyos Idrobo•Tue Dec 01 2020

This work proposes a novel approach based on OT of embeddings on the Poincar\'e model of hyperbolic spaces that relies on the gyrobarycenter mapping on M\"obius gyrovector spaces, and derives extensions to some existing Euclidean methods of OT-based domain adaptation to theirhyperbolic counterparts.

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BERTMap: A BERT-based Ontology Alignment System

Yuan He, Jiaoyan Chen, Ian Horrocks, Denvar Antonyrajah•Sat Dec 04 2021

This paper proposes a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings and first predicts mappings using a classifier based on fine-tuning the contextual embedding model BERT on text semantics corpora extracted from ontologies, and then refines the mappings through extension and repair by utilizing the ontology structure and logic.

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Ontology Matching Through Absolute Orientation of Embedding Spaces

Jan Portisch, Guilherme Costa, Karolin Stefani, K. Kreplin, M. Hladik, Heiko Paulheim•Thu Apr 07 2022

A novel structure-based mapping approach which is based on knowledge graph embeddings: the ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedding spaces.

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Truveta Mapper: A Zero-shot Ontology Alignment Framework

Sadra Naddaf-sh, Mariyam Amir, Murchana Baruah, Mahsa Eslamialishah, Sina Ehsani, Alireza Bahramali, Saman Zarandioon•Mon Jan 23 2023

A new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task that offers log-linear complexity, and overall makes the OM task efficient and more straightforward without much post-processing involving mapping extension or mapping repair.

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Contextualized Structural Self-supervised Learning for Ontology Matching

Zhu Wang•Thu Oct 05 2023

This study introduces a novel self-supervised learning OM framework with input ontologies, called LaKERMap, which capitalizes on the contextual and structural information of concepts by integrating implicit knowledge into transformers and surpasses state-of-the-art systems in terms of alignment quality and inference time.

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