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Proceedings - Federated Web Search 2014

Overview of the TREC 2014 Federated Web Search Track

Thomas Demeester, Dolf Trieschnigg, Dong Nguyen, Djoerd Hiemstra, Ke Zhou

Abstract

The TREC Federated Web Search track facilitates research on federated web search, by providing a large realistic data collection sampled from a multitude of online search engines. The FedWeb 2013 Resource Selection and Results Merging tasks are again included in FedWeb 2014, and we additionally introduced the task of vertical selection. Other new aspects are the required link between the Resource Selection and Results Merging tasks, and the importance of diversity i`n the merged results. After an overview of the new data collection and relevance judgments, the individual participants' results for the tasks are introduced, analyzed, and compared.

Bibtex 
@inproceedings{DBLP:conf/trec/DemeesterTNHZ14,
    author = {Thomas Demeester and Dolf Trieschnigg and Dong Nguyen and Djoerd Hiemstra and Ke Zhou},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {Overview of the {TREC} 2014 Federated Web Search Track},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/overview-federated.pdf},
    timestamp = {Tue, 24 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/DemeesterTNHZ14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Learning to Combine Collection-centric and Document-centric Models for Resource Selection

Krisztian Balog

Abstract

This paper describes our participation in the Federated Web Search track at TREC 2014. Our main focus is on the resource selection task, where we employ a learning-to-rank approach to combine various (instantiations of) resource ranking models. Further, we show that vertical selection can be run on the output from resource selection, and that it directly benefits from the improvements of thereof.

Bibtex 
@inproceedings{DBLP:conf/trec/Balog14,
    author = {Krisztian Balog},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {Learning to Combine Collection-centric and Document-centric Models for Resource Selection},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-NTNUiS\_federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/Balog14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

University of Padua at TREC 2014: Federated Web Search Track

Emanuele Di Buccio, Massimo Melucci

Abstract

This paper reports on the participation of the University of Padua to the TREC 2014 Federated Web Search track. The objective was the experimental investigation of the TWF·IRF weighting framework for resource and vertical selection in Federated Web Search settings.

Bibtex 
@inproceedings{DBLP:conf/trec/BuccioM14,
    author = {Emanuele Di Buccio and Massimo Melucci},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {University of Padua at {TREC} 2014: Federated Web Search Track},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-UPD\_federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/BuccioM14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Opinions in Federated Search: University of Lugano at TREC 2014 Federated Web Search Track

Anastasia Giachanou, Fabio Crestani, Ilya Markov

Abstract

This technical report presents the work carried out at the University of Lugano on TREC 2014 Federated Web Search track. The main motivation behind our approach is to provide better coverage of opinions that are present in federated resources. On the resource selection and vertical selection steps, we apply opinion mining to select opinionated resources/verticals given a user's query. We do this by combining relevance-based selection with lexicon-based opinion mining. On the results merging step, we diversify the final document ranking based on sentiment using the retrieval-interpolated diversification method.

Bibtex 
@inproceedings{DBLP:conf/trec/GiachanouCM14,
    author = {Anastasia Giachanou and Fabio Crestani and Ilya Markov},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {Opinions in Federated Search: University of Lugano at {TREC} 2014 Federated Web Search Track},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-ULugano-federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/GiachanouCM14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

ICTNET at Federated Web Search Track 2014

Feng Guan, Shuiyuan Zhang, Chunmei Liu, Xiaoming Yu, Yue Liu, Xueqi Cheng

Abstract

We have participated all the three tasks of FedWeb 2014 this year. Basic methods that we used for these tasks will be described in section 2. Section 3 shows combination of the basic methods for different runs and the results will also be introduced.

Bibtex 
@inproceedings{DBLP:conf/trec/GuanZLYLC14,
    author = {Feng Guan and Shuiyuan Zhang and Chunmei Liu and Xiaoming Yu and Yue Liu and Xueqi Cheng},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {{ICTNET} at Federated Web Search Track 2014},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-ICTNET\_federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/GuanZLYLC14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Two selfless contributions to web search evaluation

Djoerd Hiemstra, Robin Aly

Abstract

We present our results for the Web Search track and the Federated Web Search track for the 23rd Text Retrieval Conference TREC.

Bibtex 
@inproceedings{DBLP:conf/trec/HiemstraA14,
    author = {Djoerd Hiemstra and Robin Aly},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {Two selfless contributions to web search evaluation},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-ut\_web-federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/HiemstraA14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Simple May Be Best - A Simple and Effective Method for Federated Web Search via Search Engine Impact Factor Estimation

Shan Jin, Man Lan

Abstract

This paper reports our participation in the three tasks, i.e., vertical selection (VS), resource selection (RS) and results merging (RM) in TREC 2014 Federated Web Search track. In consideration of the connections between vertical and search engine (i.e., a vertical could contain multiple resources), we address the two tasks in an iterative way. Existing algorithms adopted relevance measures to calculate the semantic relatedness between query and resources or returned results. However they neglected the influence of search engine in itself. In this work, we propose a Search engine Impact Factor (SEIF) estimation approach to improve the performance of vertical and resource selection. The officially released results showed that our systems ranked 1st in RS task and 2nd in VS task.

Bibtex 
@inproceedings{DBLP:conf/trec/JinL14,
    author = {Shan Jin and Man Lan},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {Simple May Be Best - {A} Simple and Effective Method for Federated Web Search via Search Engine Impact Factor Estimation},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-ECNU\_federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/JinL14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Query Transformations for Result Merging

Shriphani Palakodety, Jamie Callan

Abstract

This paper describes Carnegie Mellon University's entry at the TREC 2014 Federated Web Search track (FedWeb14). Federated search pipelines typically have two components: (i) resource-selection, and (ii) result-merging. This work documents experiments to modify queries to merge results in the federated-search pipeline. Approaches from previous attempts at solving this problem involved custom query-document similarity scores or rank-combination methods. In this document, we explore how term-dependence models and query expansion strategies influence result-merging.

Bibtex 
@inproceedings{DBLP:conf/trec/PalakodetyC14,
    author = {Shriphani Palakodety and Jamie Callan},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {Query Transformations for Result Merging},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-CMU\_LTI\_federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/PalakodetyC14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

RUC at TREC 2014: Select Resources Using Topic Models

Qiuyue Wang, Shaochen Shi, Wei Cao

Abstract

This paper describes the work done in Renmin University of China for the Federated Web Search Track of TREC 2014. We participated in the resource selection task. We used the LDA topic modeling approach to select potentially relevant resources for a given query. The initial results are promising.

Bibtex 
@inproceedings{DBLP:conf/trec/WangSC14,
    author = {Qiuyue Wang and Shaochen Shi and Wei Cao},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {{RUC} at {TREC} 2014: Select Resources Using Topic Models},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-info\_ruc\_federated.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/WangSC14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Drexel at TREC 2014 Federated Web Search Track

Haozhen Zhao, Xiaohua Hu

Abstract

This paper reports our participation in the Federated Web Search Track in TREC 2014. We submitted 21 runs for all the three tasks: Vertical Selection (7), Resource Selection (7) and Results Merging (7). Our main purpose is to test several established resource selection methods on the new realistic FedWeb test collections. We evaluated 7 well known resource selection methods for the vertical selection and resource selection tasks. The effectiveness of these methods in the RS tasks does not carry to the VS tasks, which implies that more sophisticated algorithms and more diverse sources of evidence are needed for solving the VS task effectively. Our Results Merging experiments reveal the correlation between the performance of RM and the performance of its input RS results.

Bibtex 
@inproceedings{DBLP:conf/trec/ZhaoH14,
    author = {Haozhen Zhao and Xiaohua Hu},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {Drexel at {TREC} 2014 Federated Web Search Track},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-dragon\_federated.pdf},
    timestamp = {Fri, 09 Apr 2021 01:00:00 +0200},
    biburl = {https://dblp.org/rec/conf/trec/ZhaoH14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

University of Delaware at TREC 2014

Ashraf Bah, Karankumar Sabhnani, Mustafa Zengin, Ben Carterette

Abstract

This paper describes the work of the Information Retrieval Lab at the University of Delaware (team name “udel”) on TREC 2014 tracks. We participated in five different tracks: Contextual Suggestion, Federated Web, Microblog, Session, and Web.

Bibtex 
@inproceedings{DBLP:conf/trec/BahSZC14,
    author = {Ashraf Bah and Karankumar Sabhnani and Mustafa Zengin and Ben Carterette},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {University of Delaware at {TREC} 2014},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-udel\_cs-federated-microblog-web-sesson.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/BahSZC14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

The University of Illinois' Graduate School of Library and Information Science at TREC 2014

Garrick Sherman, Miles Efron, Craig Willis

Abstract

The University of Illinois' Graduate School of Library and Information Science (uiucGSLIS) participated in TREC's Federated Web (FedWeb) and Knowledge Base Acceleration (KBA) tracks in 2014. Specifically, we submitted runs for the FedWeb resource selection and KBA cumulative citation recommendation (CCR) tasks.

Bibtex 
@inproceedings{DBLP:conf/trec/ShermanEW14,
    author = {Garrick Sherman and Miles Efron and Craig Willis},
    editor = {Ellen M. Voorhees and Angela Ellis},
    title = {The University of Illinois' Graduate School of Library and Information Science at {TREC} 2014},
    booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, {TREC} 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
    series = {{NIST} Special Publication},
    volume = {500-308},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2014},
    url = {http://trec.nist.gov/pubs/trec23/papers/pro-uiucGSLIS-federated-kba.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/ShermanEW14.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}