MRM: An adaptive framework for XML searching

Ho Lam Lau; Wilfred Ng

doi:10.1145/1645953.1646325

MRM: An adaptive framework for XML searching

Ho Lam Lau
, Wilfred Ng

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In order to deal with the diversified nature of XML documents as well as individual user preferences, we propose a novel Multi-Ranker Model (MRM), which is able to abstract a spectrum of important XML properties and adapt the features to different XML search needs. The model consists of a novel three-level ranking structure and a training module called Ranking Support Vector Machine in a voting Spy Nä1ve Bayes Framework (RSSF). RSSF is effective in learning search preference and then ranks the returned results adaptively. In this demonstration, we present our prototype developed from the model, which we call it the MRM XML search engine. The MRM engine employs only a list of simple XML tagged keywords as a user query for searching XML fragments from a collection of real XML documents. The demonstration presents an indepth analyses of the effectiveness of adaptive rankers, tailored XML rankers and a spectrum of low level ranking features.
Copyright is held by the author/owner(s).

Original language	English
Title of host publication	ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages	2103-2104
DOIs	https://doi.org/10.1145/1645953.1646325
Publication status	Published - 2009
Externally published	Yes
Event	ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China Duration: 2 Nov 2009 → 6 Nov 2009

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Place	China
City	Hong Kong
Period	2/11/09 → 6/11/09

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Adaptive ranking
Searching
Tagged keyword
XML

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10.1145/1645953.1646325

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Cite this

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title = "MRM: An adaptive framework for XML searching",

abstract = "In order to deal with the diversified nature of XML documents as well as individual user preferences, we propose a novel Multi-Ranker Model (MRM), which is able to abstract a spectrum of important XML properties and adapt the features to different XML search needs. The model consists of a novel three-level ranking structure and a training module called Ranking Support Vector Machine in a voting Spy N{\"a}1ve Bayes Framework (RSSF). RSSF is effective in learning search preference and then ranks the returned results adaptively. In this demonstration, we present our prototype developed from the model, which we call it the MRM XML search engine. The MRM engine employs only a list of simple XML tagged keywords as a user query for searching XML fragments from a collection of real XML documents. The demonstration presents an indepth analyses of the effectiveness of adaptive rankers, tailored XML rankers and a spectrum of low level ranking features. Copyright is held by the author/owner(s). ",

keywords = "Adaptive ranking, Searching, Tagged keyword, XML",

author = "Lau, \{Ho Lam\} and Wilfred Ng",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 ; Conference date: 02-11-2009 Through 06-11-2009",

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language = "English",

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Lau, HL & Ng, W 2009, MRM: An adaptive framework for XML searching. in ACM 18th International Conference on Information and Knowledge Management, CIKM 2009. International Conference on Information and Knowledge Management, Proceedings, pp. 2103-2104, ACM 18th International Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, 2/11/09. https://doi.org/10.1145/1645953.1646325

MRM: An adaptive framework for XML searching. / Lau, Ho Lam; Ng, Wilfred.
ACM 18th International Conference on Information and Knowledge Management, CIKM 2009. 2009. p. 2103-2104 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2009

Y1 - 2009

N2 - In order to deal with the diversified nature of XML documents as well as individual user preferences, we propose a novel Multi-Ranker Model (MRM), which is able to abstract a spectrum of important XML properties and adapt the features to different XML search needs. The model consists of a novel three-level ranking structure and a training module called Ranking Support Vector Machine in a voting Spy Nä1ve Bayes Framework (RSSF). RSSF is effective in learning search preference and then ranks the returned results adaptively. In this demonstration, we present our prototype developed from the model, which we call it the MRM XML search engine. The MRM engine employs only a list of simple XML tagged keywords as a user query for searching XML fragments from a collection of real XML documents. The demonstration presents an indepth analyses of the effectiveness of adaptive rankers, tailored XML rankers and a spectrum of low level ranking features. Copyright is held by the author/owner(s).

AB - In order to deal with the diversified nature of XML documents as well as individual user preferences, we propose a novel Multi-Ranker Model (MRM), which is able to abstract a spectrum of important XML properties and adapt the features to different XML search needs. The model consists of a novel three-level ranking structure and a training module called Ranking Support Vector Machine in a voting Spy Nä1ve Bayes Framework (RSSF). RSSF is effective in learning search preference and then ranks the returned results adaptively. In this demonstration, we present our prototype developed from the model, which we call it the MRM XML search engine. The MRM engine employs only a list of simple XML tagged keywords as a user query for searching XML fragments from a collection of real XML documents. The demonstration presents an indepth analyses of the effectiveness of adaptive rankers, tailored XML rankers and a spectrum of low level ranking features. Copyright is held by the author/owner(s).

KW - Adaptive ranking

KW - Searching

KW - Tagged keyword

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ER -

MRM: An adaptive framework for XML searching

Abstract

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Conference

Bibliographical note

Research Keywords

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