Efficient Top-k Queries for XML Information Retrieval

author: Gerhard Weikum, Max Planck Institute

Categories

Top: Computer Science: Information Retrieval

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*Slides*
0:01	TITLE
1:47	Queries Beyond Google
4:30	What If The Semantic Web Existed And All Information Were in XML?
4:52	XML-IR Example (1)
5:20	XML-IR Example (2)
6:29	Outline
7:03	XML-IR: History and Related Work
8:05	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, IB
10:08	Query Expansion and Execution
13:01	Towards a Statistically Semantic Web
15:36	Outline
15:38	Efficient Top-k Search [Buckley85, Güntzer et al. 00, Fagin01]
20:35	Probabilistic Pruning of Top-k Candidates [VLDB 04]
25:37	Probabilistic Threshold Test
27:28	Performance Results for .Gov Queries
28:44	.Gov Expanded Queries
29:14	Top-k Queries with Query Expansion [SIGIR 05]
32:10	Combined Algorithm (CA) for Balanced SA/RA Scheduling [Fagin 03]
33:56	Index Access Optimization [joint work with Holger Bast, Debapriyo Majumdar, Ralf Schenkel, Martin Theobald]
35:43	Performance of SA/RA Scheduling Methods [joint work with Holger Bast, Debapriyo Majumdar, Ralf Schenkel, Martin Theobald]
36:48	Outline
36:50	TopX Search on XML Data [VLDB 05]
41:08	TopX Algorithm
41:22	TopX Query Processing By Example
41:32	Challenge: XML IR on Graphs
41:51	Experimental Results: INEX Benchmark
43:07	Challenge: XML IR on Graphs
45:13	Outline
45:16	Conclusion: Ongoing and Future Work

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