Entity Resolution in a Big Data Framework
Abstract
Entity Resolution (ER) concerns identifying logically equivalent pairs of entities that may be syntactically disparate. Although ER is a long-standing problem in the artificial intelligence community, the growth of Linked Open Data, a collection of semi-structured datasets published and inter-connected on the Web, mandates a new approach. The thesis is that building a viable Entity Resolution solution for serving Big Data needs requires simultaneously resolving challenges of automation, heterogeneity, scalability and domain independence. The dissertation aims to build such a system and evaluate it on real-world datasets published already as Linked Open Data.
Cite
Text
Kejriwal. "Entity Resolution in a Big Data Framework." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9256Markdown
[Kejriwal. "Entity Resolution in a Big Data Framework." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/kejriwal2015aaai-entity/) doi:10.1609/AAAI.V29I1.9256BibTeX
@inproceedings{kejriwal2015aaai-entity,
title = {{Entity Resolution in a Big Data Framework}},
author = {Kejriwal, Mayank},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {4243-4244},
doi = {10.1609/AAAI.V29I1.9256},
url = {https://mlanthology.org/aaai/2015/kejriwal2015aaai-entity/}
}