Booma S. Balasubramani

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We propose a semi-automatic ontology matching system using a hybrid active learning and online learning approach. Following the former paradigm, those mappings whose validation is estimated to lead to greater quality gain are selected for user validation, a process that occurs in each iteration, following the online learning paradigm. Experimental results(More)
Cities are actively creating open data portals to enable predictive analytics of urban data. However, the large number of observable patterns that can be extracted as rules by techniques such as Association Rule Mining (ARM) makes the task of sifting through patterns a tedious and time-consuming task. In this paper, we explore the use of domain ontologies(More)
AgreementMakerLight (AML) is an automated ontology matching system based primarily on element-level matching and on the use of external resources as background knowledge. This paper describes its configuration for the OAEI 2016 competition and discusses its results. For this OAEI edition, we tackled instance matching for the first time, thus expanding the(More)
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