The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologi.pdfVIP

The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologies on the Semantic Web AnHai Doan 1 , Jayant Madhavan 2 , Robin Dhamankar 1 , Pedro Domingos 2 , Alon Halevy 2 1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA {anhai,dhamanka}@ 2 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA {jayant,pedrod,alon}@ Received: date / Revised version: date Abstract On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the seman- tic mappings between them. 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 ontol- ogy mapping process is crucial to the success of the Seman- tic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two on- tologies, 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 similar- ity measures, and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learn- ing strategies, each of which exploits well a different type of information either in the data instances or in the taxo- nomic structure of the ontologies. To further improve match- ing 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 va- riety 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. Finally, we extend GLUE to find complex mappings between ontolo- gies, and describe experimen