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ParisMatch Ontology Matching at Scale

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Academic year: 2022

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ParisMatch

Ontology Matching at Scale

Antoine Amarilli Pierre Senellart Fabian M. Suchanek

el´ecom ParisTech

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Performance

Millions of entities,tens of millionsof facts.

In-memory,multithreaded computation.

BerkeleyDB, SSD RAM, 1 thread RAM, 4 threads

20h30 1h15 26m

Table : Running times for the DBpedia–Yagoalignment task.

Intel Core i7-3820 CPU clocked at 3.60 Ghz, 48 GB of RAM.

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Join Relations

a:Douglas_Adams a:countryOfBirth a:UK

b:Douglas_Adams

b:Cambridge b:birthPlace

(b:birthPlace, b:country) b:UK

b:country

Simplest possible difference in structure between ontologies.

Relations of one ontology correspond to join relationsin the other ontology.

Similar to the “join” operator of relational calculus.

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Literal Similarity Functions

Edgar R. Burroughs Douglas Adams and Constance Garnett Edgar Rice Burroughs Adams, Douglas Constance Garnett

The original Parisuses an exactliteral equality function.

Ad-hoc refinements: adjust for case, strip special characters...

More general : index the literals usingshingling : hash the shingle sets through random hash functions keep the minimum values (MinHashing)

Use the index to computepairs of similar literals efficiently!

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