Affiliations: [a] Department of Computer Science, Aalborg University, Denmark. E-mails: jacobo@rouces.org, khose@cs.aau.dk | [b] Department of Computer Science, Rutgers University–New Brunswick, USA. E-mail: gdm@demelo.org
Note: [1] The research leading to these results has received funding from the Danish Council for Independent Research (DFF) under grant agreement No. DFF-4093-00301, as well as the DARPA SocialSim program.
Abstract: An increasing number of structured knowledge bases have become available on the Web, enabling many new forms of analyses and applications. However, the fact that the data is being published by different parties with different vocabularies and ontologies means that there is a high degree of heterogeneity and no common schema. At the same time, the abundance of different human languages across unstructured data presents a similar problem, because most text mining tools only cater to the English language. This paper presents solutions for these two kinds of heterogeneity. It introduces Klint, a Web-based system that automatically creates mappings to transform knowledge from heterogeneous sources into FrameBase, which is a broad linked data schema that enables the representation of a wide range of knowledge. With Klint, a user can review and edit the mappings with a streamlined interface, which in turn allows for human-level accuracy with minimum human effort. The paper further describes how FrameBase can be extended to support multilingual labels, which can aid in extending current tools for integrating English text into FrameBase knowledge.
Keywords: Linked data, data integration, multilingual text mining
