Scalable Semantic Retrieval Through Summarization and Refinement
Abstract
malli @ cn.ibm.com Query processing of OWL-DL ontologies is intractable in the worst case, but we present a novel technique that in practice allows for efficient querying of ontologies with large Aboxes in secondary storage. We focus on the processing of instance retrieval queries, i.e., queries that retrieve individuals in the Abox which are instances of a given concept C. Our technique uses summarization and refinement to reduce instance retrieval to a small relevant subset of the original Abox. We demonstrate the effectiveness of this technique in Aboxes with up to 7 million assertions. Our results are applicable to the very expressive description logic SHIN, which corresponds to OWL-DL minus nominals and datatypes.
Cite
Text
Dolby et al. "Scalable Semantic Retrieval Through Summarization and Refinement." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Dolby et al. "Scalable Semantic Retrieval Through Summarization and Refinement." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/dolby2007aaai-scalable/)BibTeX
@inproceedings{dolby2007aaai-scalable,
title = {{Scalable Semantic Retrieval Through Summarization and Refinement}},
author = {Dolby, Julian and Fokoue, Achille and Kalyanpur, Aditya and Kershenbaum, Aaron and Schonberg, Edith and Srinivas, Kavitha and Ma, Li},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {299-304},
url = {https://mlanthology.org/aaai/2007/dolby2007aaai-scalable/}
}