Cited By
View all- Golenberg KSagiv Y(2016)Constructing Data Graphs for Keyword SearchDatabase and Expert Systems Applications10.1007/978-3-319-44406-2_33(399-409)Online publication date: 6-Aug-2016
A personal perspective on keyword search over data graphs
Pages 21 - 32
Abstract
Theoretical and practical issues pertaining to keyword search over data graphs are discussed. A formal model and algorithms for enumerating answers (by operating directly on the data graph) are described. Various aspects of a system are explained, including the object-connector-property data model, how it is used to construct a data graph from an XML document, how to deal with redundancies in the source data, what are duplicate answers, implementation and GUI. An approach to ranking that combines textual relevance with semantic considerations is described. It is argued that search over data graphs is inherently a two-dimensional process, where the goal is not just to find particular content but also to collect information on how the desired data may be semantically connected.
References
[1]
H. Achiezra. Understanding Complex Answers of Queries on Graphs. Master's thesis, Hebrew University, Department of Computer Science, 2009.
[2]
H. Achiezra, K. Golenberg, B. Kimelfeld, and Y. Sagiv. Exploratory keyword search on data graphs. In SIGMOD Conference, pages 1163--1166, 2010.
[3]
M. S. Ackerman. The politics of design: Next generation computational environments. In K. Kraemer and M. Elliott, editors, Computerization Movements and Technology Diffusion: From Mainframes to Ubiquitous Computing, ASIS&T Monograph Series, 2006.
[4]
S. Agrawal, S. Chaudhuri, and G. Das. Dbxplorer: enabling keyword search over relational databases. In SIGMOD Conference, page 627, 2002.
[5]
G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using banks. In ICDE, pages 431--440, 2002.
[6]
V. Bicer, T. Tran, and R. Nedkov. Ranking support for keyword search on structured data using relevance models. In CIKM, pages 1669--1678, 2011.
[7]
J. Coffman and A. C. Weaver. A framework for evaluating database keyword search strategies. In CIKM, pages 729--738, 2010.
[8]
S. Cohen, I. Fadida, Y. Kanza, B. Kimelfeld, and Y. Sagiv. Full disjunctions: Polynomial-delay iterators in action. In VLDB, pages 739--750, 2006.
[9]
S. Cohen, J. Mamou, Y. Kanza, and Y. Sagiv. Xsearch: A semantic search engine for xml. In VLDB, pages 45--56, 2003.
[10]
R. Fagin, A. O. Mendelzon, and J. D. Ullman. A simplified universal relation assumption and its properties. ACM Trans. Database Syst., 7(3):343--360, 1982.
[11]
C. A. Galindo-Legaria. Outerjoins as disjunctions. In SIGMOD Conference, pages 348--358, 1994.
[12]
K. Golenberg, B. Kimelfeld, and Y. Sagiv. Keyword proximity search in complex data graphs. In SIGMOD Conference, pages 927--940, 2008.
[13]
K. Golenberg, B. Kimelfeld, and Y. Sagiv. Optimizing and parallelizing ranked enumeration. PVLDB, 4(11):1028--1039, 2011.
[14]
K. Golenberg and Y. Sagiv. The architecture of a system for keyword search over data graphs. Manuscript in preparation.
[15]
K. Golenberg and Y. Sagiv. A practically efficient algorithm for generating all answers to search queries over data graphs. Manuscript in preparation.
[16]
D. Hiemstra. Statistical language models for intelligent XML retrieval. In Intelligent Search on XML Data, pages 107--118, 2003.
[17]
V. Hristidis and Y. Papakonstantinou. Discover: Keyword search in relational databases. In VLDB, pages 670--681, 2002.
[18]
D. S. Johnson, C. H. Papadimitriou, and M. Yannakakis. On generating all maximal independent sets. Inf. Process. Lett., 27(3):119--123, 1988.
[19]
V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan, R. Desai, and H. Karambelkar. Bidirectional expansion for keyword search on graph databases. In VLDB, pages 505--516, 2005.
[20]
B. Kimelfeld and Y. Sagiv. Efficient engines for keyword proximity search. In WebDB, pages 67--72, 2005.
[21]
B. Kimelfeld and Y. Sagiv. Efficiently enumerating results of keyword search. In DBPL, pages 58--73, 2005.
[22]
B. Kimelfeld and Y. Sagiv. Finding and approximating top-k answers in keyword proximity search. In PODS, pages 173--182, 2006.
[23]
B. Kimelfeld and Y. Sagiv. Efficiently enumerating results of keyword search over data graphs. Inf. Syst., 33(4-5):335--359, 2008.
[24]
B. Kimelfeld and Y. Sagiv. Finding a minimal tree pattern under neighborhood constraints. In PODS, pages 235--246, 2011.
[25]
B. Kimelfeld and Y. Sagiv. Extracting minimum-weight tree patterns from a schema with neighborhood constraints. In ICDT, 2013.
[26]
E. L. Lawler. A procedure for computing the k best solutions to discrete optimization problems and its application to the shortest path problem. Management Science, 18(7), 1972.
[27]
Y. Lv and C. Zhai. Positional language models for information retrieval. In SIGIR, pages 299--306, 2009.
[28]
Y. Mass, M. Ramanath, Y. Sagiv, and G. Weikum. IQ: The case for iterative querying for knowledge. In CIDR, pages 38--44, 2011.
[29]
Y. Mass and Y. Sagiv. Language models for virtual documents in data graphs. Unpublished manuscript.
[30]
Y. Mass and Y. Sagiv. Language models for keyword search over data graphs. In WSDM, pages 363--372, 2012.
[31]
K. G. Murty. An algorithm for ranking all the assignments in order of increasing cost. Operations Research, 16(3), 1968.
[32]
J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In SIGIR, pages 275--281, 1998.
[33]
A. Rajaraman and J. D. Ullman. Integrating information by outerjoins and full disjunctions. In PODS, pages 238--248, 1996.
[34]
Y. Sagiv. Can we use the universal instance assumption without using nulls? In SIGMOD Conference, pages 108--120, 1981.
[35]
Y. Sagiv. A characterization of globally consistent databases and their correct access paths. ACM Trans. Database Syst., 8(2):266--286, 1983.
[36]
T. Tran, H. Wang, S. Rudolph, and P. Cimiano. Top-k exploration of query candidates for efficient keyword search on graph-shaped (rdf) data. In ICDE, pages 405--416, 2009.
[37]
C. Zhai and J. D. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst., 22(2):179--214, 2004.
Index Terms
- A personal perspective on keyword search over data graphs
Recommendations
Knowledge Management for Keyword Search over Data Graphs
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementThis demo presents exploratory keyword search over data graphs by means of semantic facets. The demo starts with a keyword search over data graphs. Answers are first ranked by an existing search engine that considers their textual relevance and semantic ...
Split keyword fuzzy and synonym search over encrypted cloud data
A substitute solution for various organizations of data owners to store their data in the cloud using storage as a service(SaaS). The outsourced sensitive data is encrypted before uploading into the cloud to achieve data privacy. The encrypted data is ...
Public-Key Encryption with Fuzzy Keyword Search: A Provably Secure Scheme under Keyword Guessing Attack
Public-key encryption with keyword search (PEKS) is a versatile tool. It allows a third party knowing the search trapdoor of a keyword to search encrypted documents containing that keyword without decrypting the documents or knowing the keyword. However,...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
March 2013
301 pages
ISBN:9781450315982
DOI:10.1145/2448496
Copyright © 2013 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 March 2013
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
EDBT/ICDT '13
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 206Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Nov 2024
Other Metrics
Citations
Cited By
View all- Golenberg KSagiv Y(2016)Constructing Data Graphs for Keyword SearchDatabase and Expert Systems Applications10.1007/978-3-319-44406-2_33(399-409)Online publication date: 6-Aug-2016
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in