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Taming Wild Big Data

Authors: Jennifer Sleeman, and Tim Finin

Book Title: AAAI Fall Symposium on Natural Language Access to Big Data

Date: November 13, 2014

Abstract: Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph’s nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.

Type: InProceedings

Publisher: AAAI Press

Tags: big data, rdf, semantic web, learning

Google Scholar: search

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