Taming Wild Big Data
| dc.contributor.author | Sleeman, Jennifer | |
| dc.contributor.author | Finin, Tim | |
| dc.date.accessioned | 2018-11-01T16:45:14Z | |
| dc.date.available | 2018-11-01T16:45:14Z | |
| dc.date.issued | 2014-11-13 | |
| dc.description | AAAI Fall Symposium on Natural Language Access to Big Data | en_US |
| dc.description.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. | en_US |
| dc.description.uri | https://ebiquity.umbc.edu/paper/html/id/672/Taming-Wild-Big-Data | en_US |
| dc.format.extent | 4 pages | en_US |
| dc.genre | conference papar pre-print | en_US |
| dc.identifier | doi:10.13016/M25717S1P | |
| dc.identifier.citation | Jennifer Sleeman and Tim Finin, Taming Wild Big Data, AAAI Fall Symposium on Natural Language Access to Big Data, Nov. 2014, https://ebiquity.umbc.edu/paper/html/id/672/Taming-Wild-Big-Data | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/11827 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AAAI Press | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | big data | en_US |
| dc.subject | learning | en_US |
| dc.subject | Semantic Web | en_US |
| dc.subject | Resource description framework (rdf) | en_US |
| dc.subject | UMBC Ebiquity Research Group | en_US |
| dc.title | Taming Wild Big Data | en_US |
| dc.type | Text | en_US |