document: Learning to map between ontologies on the semantic web Ontologies play a prominent role on the Semantic Web . They make possible the widespread publication of machine understandable data , opening myriad opportunities for automated information processing . However , because of the Semantic Web 's distributed nature , data on it will inevitably come from many different ontologies . Information processing across ontologies is not possible without knowing the semantic mappings between their elements . Manually finding such mappings is tedious , error-prone , and clearly not possible at the Web scale . Hence , the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web . We describe glue , a system that employs machine learning techniques to find such mappings . Given two ontologies , for each concept in one ontology glue finds the most similar concept in the other ontology . We give well-founded probabilistic definitions to several practical similarity measures , and show that glue can work with all of them . This is in contrast to most existing approaches , which deal with a single similarity measure . Another key feature of glue is that it uses multiple learning strategies , each of which exploits 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 . For this purpose , we show that relaxation labeling , a well-known constraint optimization technique used in computer vision and other fields , can be adapted to work efficiently in our context . Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently 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 .
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