HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3705-3710.
Abstract
The rapid growth of open data on the Web promotes the development of data portals that facilitate finding useful datasets. To help users quickly inspect a dataset found in a portal, we propose to summarize its contents and generate a hierarchical grouping of entities connected by relations. Our generic approach, called HIEDS, considers coverage of dataset, height of hierarchy, cohesion within groups, overlap between groups, and homogeneity of groups, and integrates these configurable factors into a combinatorial optimization problem to solve. We present an efficient solution, to serve users with dynamically configured summaries with acceptable latency. We systematically experiment with our approach on real-world RDF datasets.
@inproceedings{ijcai2016p521,
title = {HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization},
author = {Cheng, Gong and Jin, Cheng and Qu, Yuzhong},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3705--3710},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/521},
}
TY - CPAPER TI - HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization AU - Cheng, Gong AU - Jin, Cheng AU - Qu, Yuzhong T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3705 EP - 3710 UR - https://ijcai.org/proceedings/2016/521 ER -Copied
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