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Efficient Top-k Queries for XML Information Retrieval
Published on Feb 25, 20075580 Views
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Chapter list
TITLE00:01
Queries Beyond Google01:47
What If The Semantic Web Existed And All Information Were in XML?04:30
XML-IR Example (1)04:52
XML-IR Example (2)05:20
Outline06:29
XML-IR: History and Related Work07:03
XML-IR Concepts [MPII XXL 00 & TopX 05, U Duisburg XIRQL 00, U Dublin Elixir 00, Cornell XRank & Quark 03, U Michigan 02, U Wisconsin 04, CWI Cirquid 03, AT&T FleXPath 04, W3C XPath Full-Text 05, IB08:05
Query Expansion and Execution10:08
Towards a Statistically Semantic Web13:01
Outline15:36
Efficient Top-k Search [Buckley85, Güntzer et al. 00, Fagin01]15:38
Probabilistic Pruning of Top-k Candidates [VLDB 04]20:35
Probabilistic Threshold Test25:37
Performance Results for .Gov Queries27:28
.Gov Expanded Queries28:44
Top-k Queries with Query Expansion [SIGIR 05]29:14
Combined Algorithm (CA) for Balanced SA/RA Scheduling [Fagin 03]32:10
Index Access Optimization [joint work with Holger Bast, Debapriyo Majumdar, Ralf Schenkel, Martin Theobald] 33:56
Performance of SA/RA Scheduling Methods [joint work with Holger Bast, Debapriyo Majumdar, Ralf Schenkel, Martin Theobald]35:43
Outline36:48
TopX Search on XML Data [VLDB 05]36:50
TopX Algorithm41:08
TopX Query Processing By Example41:22
Challenge: XML IR on Graphs41:32
Experimental Results: INEX Benchmark41:51
Challenge: XML IR on Graphs43:07
Outline45:13
Conclusion: Ongoing and Future Work45:16