Similarity flooding: a versatile graph matching algorithm and its application to schema matching

By Sergey Melnik, Hector Garcia-Molina, Erhard Rahm. Proceedings / International Conference on Data Engineering, 2002

1,650 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/aa1158b0ba07da121e4f2ab039bff2c03c929616

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pavel Shvaiko
2. Jérôme Euzenat
3. Natasha Noy
4. Erhard Rahm
5. Jayant Madhavan
6. Philip A. Bernstein
7. Mark A. Musen
8. Sergey Melnik

In Computer Networks and Communications

1. Natasha Noy
2. Jayant Madhavan
3. Philip A. Bernstein
4. Jérôme Euzenat
5. Sergey Melnik
6. Pavel Shvaiko
7. Erhard Rahm
8. Mark A. Musen

In Management Science and Operations Research

1. Natasha Noy
2. Erhard Rahm
3. Philip A. Bernstein
4. Sergey Melnik
5. Jayant Madhavan
6. Mark A. Musen
7. AnHai Doan
8. Ming Mao

Citations in the corpus

• GMO: A Graph Matching for Ontologies (2005, background): “Some similaritybased approaches to ontology matching have been proposed in the literatures [3, 4, 8, 10, 12, 14].”
• Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR) (2020, methodology): “Then considering specific graph architecture different structures can be classified using graph similarity measure i.e. similarity flooding algorithm [99], SimRank algorithm [100], Graph similarity scoring [101] and vertex similarity method [102].”
• Cleaning Framework for Big Data - Object Identification and Linkage (2015, background): “These data cleaning methods have both advantages and disadvantages, and are applicable to their own limited areas.”
• Graph Matching Algorithms for Business Process Model Similarity Search (2009, methodology): “[12] use an algorithm known as ‘similarity flooding’ [13].”
• Ontology Mapping: An Information Retrieval and Interactive Activation Network Based Approach (2007, methodology): “, analyzing linguistic information of elements in ontologies [15], treating ontologies as structural graphs [12], applying heuristic rules to look for specific mapping patterns [8] and machine learning techniques [1].”
• Rondo: a programming platform for generic model management (2003, methodology): “As explained in [17], the multimap returned by the algorithm may contain a large fraction of the cross product of the nodes in m1 and m2, and needs to be filtered.”
• Similarity of business process models: Metrics and evaluation (2011, methodology): “It is based on the similarity flooding algorithm [40] that is introduced from the area of matching database schemas.”
• PIDGIN: ontology alignment using web text as interlingua (2013, background): “Secondly, Similarity Flooding requires both ontologies to have similar edge types, while PIDGIN doesn’t impose any such strong requirement.”