@Article{doan04a,
  author =	 {AnHai Doan and Jayant Madhavan and Robin Dhamankar
                  and Pedro Domingos and Alon Halevy},
  title =	 {Learning to match ontologies on the SemanticWeb},
  journal =	 {The {VLDB} Journal},
  year =	 2004,
  volume =	 12,
  pages =	 {303--319},
  abstract =	 {On the Semantic Web, data will inevitably come from
                  many different ontologies, and information
                  processing across ontologies is not possible without
                  knowing the semantic mappings between them. Manually
                  nding such mappings is tedious, error-prone, and
                  clearly not possible on the Web scale. Hence the
                  development of tools to assist in the ontology
                  mapping process is crucial to the success of the
                  SemanticWeb. We describe GLUE, a system that employs
                  machine learning techniques to nd such
                  mappings. Given two ontologies, for each concept in
                  one ontology GLUE nds the most similar concept in
                  the other ontology. We give well-founded
                  probabilistic de nitions to several practical
                  similarity measures and show that GLUE can work with
                  all of them. Another key feature of GLUE is that it
                  uses multiple learning strategies, each of which
                  exploits well a different type of information either
                  in the data instances or in the taxonomic structure
                  of the ontologies. To further improve matching
                  accuracy, we extend GLUE to incorporate commonsense
                  knowledge and domain constraints into the matching
                  process. Our approach is thus distinguished in that
                  it works with a variety of well-de ned similarity
                  notions and that it ef ciently incorporates multiple
                  types of knowledge.We describe a set of experiments
                  on several real-world domains and show that GLUE
                  proposes highly accurate semantic mappings. Finally,
                  we extend GLUE to nd complex mappings between
                  ontologies and describe experiments that show the
                  promise of the approach.},
  keywords =     {ontologies sweb},
  doi =		 {10.1007/s00778-003-0104-2},
  url =		 {http://jmvidal.cse.sc.edu/library/doan04a.pdf},
  cluster = 	 {15488225262930583838},
}