SEMANTIC MINING OF DOCUMENTS IN A RELATIONAL DATABASE

Kunal Mukerjee, Todd Porter, Sorin Gherman

2011

Abstract

Automatically mining entities, relationships, and semantics from unstructured documents and storing these in relational tables, greatly simplifies and unifies the work flows and user experiences of database products at the Enterprise. This paper describes three linear scale, incremental, and fully automatic semantic mining algorithms that are at the foundation of the new Semantic Platform being released in the next version of SQL Server. The target workload is large (10 – 100 million) enterprise document corpuses. At these scales, anything short of linear scale and incremental is costly to deploy. These three algorithms give rise to three weighted physical indexes: Tag Index (top keywords in each document); Document Similarity Index (top closely related documents given any document); and Phrase Similarity Index (top semantically related phrases, given any phrase), which are then query-able through the SQL interface. The need for specifically creating these three indexes was motivated by observing typical stages of document research, and gap analysis, given current tools and technology at the Enterprise. We describe the mining algorithms and architecture, and outline some compelling user experiences that are enabled by these indexes.

Paper Citation

in Harvard Style

Mukerjee K., Porter T. and Gherman S. (2011). SEMANTIC MINING OF DOCUMENTS IN A RELATIONAL DATABASE . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 138-150. DOI: 10.5220/0003631401460158

in Bibtex Style

@conference{kdir11,
author={Kunal Mukerjee and Todd Porter and Sorin Gherman},
title={SEMANTIC MINING OF DOCUMENTS IN A RELATIONAL DATABASE},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={138-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003631401460158},
isbn={978-989-8425-79-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - SEMANTIC MINING OF DOCUMENTS IN A RELATIONAL DATABASE
SN - 978-989-8425-79-9
AU - Mukerjee K.
AU - Porter T.
AU - Gherman S.
PY - 2011
SP - 138
EP - 150
DO - 10.5220/0003631401460158