research-article

Annotating database schemas to help enterprise search

Editors: Chen Li, Volker Markl Authors:

Eli Cortez,

Philip A. Bernstein,

Yeye He,

Lev NovikAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 8, Issue 12

Pages 1936 - 1939

https://doi.org/10.14778/2824032.2824105

Published: 01 August 2015 Publication History

Get Access

Abstract

In large enterprises, data discovery is a common problem faced by users who need to find relevant information in relational databases. In this scenario, schema annotation is a useful tool to enrich a database schema with descriptive keywords. In this paper, we demonstrate Barcelos, a system that automatically annotates corporate databases. Unlike existing annotation approaches that use Web oriented knowledge bases, Barcelos mines enterprise spreadsheets to find candidate annotations. Our experimental evaluation shows that Barcelos produces high quality annotations; the top-5 have an average precision of 87%.

References

[1]

K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In SIGMOD, pages 1247--1250, 2008.

Crossref

Google Scholar

[2]

M. J. Cafarella, A. Halevy, D. Z. Wang, E. Wu, and Y. Zhang. Webtables: exploring the power of tables on the web. Proceedings of VLDB, (1):538--549, 2008.

Crossref

Google Scholar

[3]

M. J. Cafarella, J. Madhavan, and A. Y. Halevy. Web-scale extraction of structured data. SIGMOD Record, pages 55--61, 2008.

Crossref

Google Scholar

[4]

S. Chaudhuri, V. Ganti, and R. Kaushik. A primitive operator for similarity joins in data cleaning. In Proceedings of ICDE, page 5, 2006.

Crossref

Google Scholar

[5]

J. Fan, M. Lu, B. C. Ooi, W.-C. Tan, and M. Zhang. A hybrid machine-crowdsourcing system for matching web tables. In Proceedings of ICDE, pages 976--987, 2014.

Google Scholar

[6]

R. Pimplikar and S. Sarawagi. Answering table queries on the web using column keywords. Proceedings of the VLDB Endowment, 5(10):908--919, 2012.

Crossref

Google Scholar

[7]

F. M. Suchanek, G. Kasneci, and G. Weikum. Yago: A core of semantic knowledge unifying wordnet and wikipedia. In WWW, pages 697--706, 2007.

Crossref

Google Scholar

[8]

P. Venetis, A. Halevy, J. Madhavan, M. Paşca, W. Shen, F. Wu, G. Miao, and C. Wu. Recovering semantics of tables on the web. Proceedings of VLDB, (9):528--538, 2011.

Crossref

Google Scholar

[9]

W. Wu, H. Li, H. Wang, and K. Q. Zhu. Probase: A probabilistic taxonomy for text understanding. In SIGMOD, pages 481--492, 2012.

Crossref

Google Scholar

Cited By

View all

Chai CTang NFan JLuo YDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Demystifying Artificial Intelligence for Data PreparationCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589406(13-20)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589406
Verberne SHe JKruschwitz UWiggers GLarsen BRussell-Rose Tde Vries A(2019)First International Workshop on Professional SearchACM SIGIR Forum10.1145/3308774.330879952:2(153-162)Online publication date: 17-Jan-2019
https://dl.acm.org/doi/10.1145/3308774.3308799
Zhao CHe Y(2019)Auto-EM: End-to-end Fuzzy Entity-Matching using Pre-trained Deep Models and Transfer LearningThe World Wide Web Conference10.1145/3308558.3313578(2413-2424)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313578
Show More Cited By

Annotating database schemas to help enterprise search
1. Information systems
  1. Data management systems
  2. Information retrieval

Recommendations

Annotating abstract anaphora

In this paper, we present first results from annotating abstract (discourse-deictic) anaphora in German. Our annotation guidelines provide linguistic tests for identifying the antecedent, and for determining the semantic types of both the antecedent and ...
Annotating Search Results from Web Databases

An increasing number of databases have become web accessible through HTML form-based search interfaces. The data units returned from the underlying database are usually encoded into the result pages dynamically for human browsing. For the encoded data ...
Annotating temporal information: from theory to practice
HLT '02: Proceedings of the second international conference on Human Language Technology Research

This paper expands on and updates a report on a set of guidelines for annotating time expressions [3]. Recent developments in the annotation guidelines, development of corpora, and annotation software are described, along with results on inter-annotator ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 8, Issue 12

Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii

August 2015

728 pages

ISSN:2150-8097

Editors:
Chen Li
University of California, Irvine
,
Volker Markl
TU Berlin

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 August 2015

Published in PVLDB Volume 8, Issue 12

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
94
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chai CTang NFan JLuo YDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Demystifying Artificial Intelligence for Data PreparationCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589406(13-20)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589406
Verberne SHe JKruschwitz UWiggers GLarsen BRussell-Rose Tde Vries A(2019)First International Workshop on Professional SearchACM SIGIR Forum10.1145/3308774.330879952:2(153-162)Online publication date: 17-Jan-2019
https://dl.acm.org/doi/10.1145/3308774.3308799
Zhao CHe Y(2019)Auto-EM: End-to-end Fuzzy Entity-Matching using Pre-trained Deep Models and Transfer LearningThe World Wide Web Conference10.1145/3308558.3313578(2413-2424)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313578
Yan CHe YDas GJermaine CBernstein P(2018)Synthesizing Type-Detection Logic for Rich Semantic Data Types using Open-source CodeProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196888(35-50)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3196888
Li KHe YGanjam KMatwin SYu SFarooq F(2017)Discovering Enterprise Concepts Using Spreadsheet TablesProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098102(1873-1882)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3098102
Wang YHe YChirkova RYang JSuciu D(2017)Synthesizing Mapping Relationships Using Table CorpusProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064010(1117-1132)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064010

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Annotating abstract anaphora

Annotating Search Results from Web Databases

Annotating temporal information: from theory to practice