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A virtual “Werkstatt” for digitization in the sciences
ABECTO: An ABox Evaluation and Comparison Tool for Ontologies
Combining Image and Caption Analysis for Classifying Charts in Biodiversity Texts
CoMerger: A Customizable Online Tool for Building a Consistent Quality-Assured Merged Ontology
How good is this merged ontology?
JenTab: Matching Tabular Data to Knowledge Graphs
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
Matching Biodiversity and Ecology Ontologies: Challenges & Evaluation Results
Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3
OGC Citizen Science Interoperability Experiment Engineering Report
Ontology Based Natural Language Queries Transformation into SPARQL Queries
Ontology Modularization with OAPT
Participatory Visualization Design as an Approach to Minimize the Gap between Research and Application
ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Results of the Ontology Alignment Evaluation Initiative 2020
ScholarLensViz: A Visualization Framework for Transparency in Semantic User Profiles
Semantics-driven Keyword Search over Knowledge Graphs
Tag Me If You Can! Semantic Annotation of Biodiversity Metadata with the QEMP Corpus and the BiodivTagger
Toward OWL Restriction Reconciliation in Merging Knowledge
Towards a Core Ontology for Hierarchies of Hypotheses in Invasion Biology
Towards Multiple Ontology Merging with CoMerger
Towards Transforming Tabular Datasets into Knowledge Graphs
Understanding Intraspecific Trait Variability Using Digital Herbarium Specimen Images
What to Do When the Users of an Ontology Merging System Want the Impossible? Towards Determining Compatibility of Generic Merge Requirements
Towards Multiple Ontology Merging with CoMerger
Title: | Towards Multiple Ontology Merging with CoMerger |
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Authors: | Samira Babalou, Birgitta König-Ries |
Place: | 19th International Semantic Web Conference |
Date: | 2020-11-10 |
Type: | Poster |
Abstract: |
To obtain a knowledge graph representing a domain of interest, it is often necessary to combine several, independently developed ontologies. Existing approaches are mostly limited to binary merge and lack scalability. This paper presents the development of an efficient multiple ontologies merging method, CoMerger. For efficient processing, rather than directly merging a large number of ontologies, we group related concepts across ontologies into partitions and merge first within and then across those partitions. Experiments on real-life datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. Our implementation is available through a live web portal. |