NeCO: Ontology Alignment using Near-miss Clone Detection
Geesaman, Paul Louis
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The Semantic Web is an endeavour to enhance the web with the ability to represent knowledge. The knowledge is expressed through what are called ontologies. In order to make ontologies useful, it is important to be able to match the knowledge represented in different ontologies. This task is commonly known as ontology alignment. Ontology alignment has been studied, but it remains an open problem with an annual competition dedicated to measure alignment tools' performance. Many alignment tools are computationally heavy, require training, or are useful in a specific field of study. We propose an ontology alignment method, NeCO, that builds on clone detection techniques to align ontologies. NeCO inherits the clone detection features, and it is light-weight, does not require training, and is useful for any ontology.