research-article

Taking chemistry to the task: personalized queries for chemical digital libraries

Authors:

Sascha Tönnies,

Benjamin Köhncke,

Wolf-Tilo BalkeAuthors Info & Claims

JCDL '11: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries

Pages 325 - 334

https://doi.org/10.1145/1998076.1998137

Published: 13 June 2011 Publication History

Abstract

Nowadays, the information access is conducted almost exclusively using the Web. Simple keyword based Web search engines, e.g. Google or Yahoo!, offer suitable retrieval and ranking features. In contrast, for highly specialized domains, represented by digital libraries, these features are insufficient. Considering the domain of chemistry, where searching for relevant literature is essentially centered on chemical entities. Beside commercial information providers such as Chemical Abstract Service (CAS) numerous groups are working on building free chemical search engines to overcome the expensive access to chemical literature. However, due to the nature of chemical queries these are often overspecialized. Often we need meaningful similarity measures for chemical entities for query relaxation. In chemistry, the similarity measures are vast; more than 40 similarity measures are available and focus on different aspects of chemical entities. This vast number of similarity measures is obvious, because the desired search results highly depend on the working field of the chemist. In this paper we present a personalized retrieval system for chemical documents taking into account the background knowledge of the individual chemist. This is done by a query relaxation for chemical entities using similar substances. We evaluate our approach extensively by analyzing the correlation of commonly used chemical similarity measures and fingerprint representations. All uncorrelated measures are finally used by our feedback engine to learn preferred similarity measures for each user. We also conducted a user study with domain experts showing that our system can assign a unique similarity measure for 75% of the users after only 10 feedback cycles.

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Cited By

Khavidaki SRezaei Sharifabadi SGhaebi A(2022)Services personalization in digital academic libraries: a Delphi studyDigital Library Perspectives10.1108/DLP-03-2022-001939:1(39-61)Online publication date: 7-Sep-2022
https://doi.org/10.1108/DLP-03-2022-0019
Wawrzinek JBalke W(2017)Semantic Facettation in Pharmaceutical Collections Using Deep Learning for Active Substance ContextualizationDigital Libraries: Data, Information, and Knowledge for Digital Lives10.1007/978-3-319-70232-2_4(41-53)Online publication date: 3-Nov-2017
https://doi.org/10.1007/978-3-319-70232-2_4
Köhncke BBalke W(2013)Context-Sensitive Ranking Using Cross-Domain Knowledge for Chemical Digital LibrariesResearch and Advanced Technology for Digital Libraries10.1007/978-3-642-40501-3_29(285-296)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40501-3_29
Show More Cited By

Index Terms

Taking chemistry to the task: personalized queries for chemical digital libraries
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

JCDL '11: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries

June 2011

500 pages

ISBN:9781450307444

DOI:10.1145/1998076

General Chair:
Glen Newton
Carleton University, Canada
,
Program Chairs:
Michael Wright
UCAR/NCAR, USA
,
Lillian Cassel
Villanova University, USA

Copyright © 2011 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]

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Publication History

Published: 13 June 2011

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JCDL '11

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JCDL '11: Joint Conference on Digital Libraries

June 13 - 17, 2011

Ontario, Ottawa, Canada

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

Khavidaki SRezaei Sharifabadi SGhaebi A(2022)Services personalization in digital academic libraries: a Delphi studyDigital Library Perspectives10.1108/DLP-03-2022-001939:1(39-61)Online publication date: 7-Sep-2022
https://doi.org/10.1108/DLP-03-2022-0019
Wawrzinek JBalke W(2017)Semantic Facettation in Pharmaceutical Collections Using Deep Learning for Active Substance ContextualizationDigital Libraries: Data, Information, and Knowledge for Digital Lives10.1007/978-3-319-70232-2_4(41-53)Online publication date: 3-Nov-2017
https://doi.org/10.1007/978-3-319-70232-2_4
Köhncke BBalke W(2013)Context-Sensitive Ranking Using Cross-Domain Knowledge for Chemical Digital LibrariesResearch and Advanced Technology for Digital Libraries10.1007/978-3-642-40501-3_29(285-296)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40501-3_29
Köhncke BTönnies SBalke W(2012)Catching the drift --- indexing implicit knowledge in chemical digital librariesProceedings of the Second international conference on Theory and Practice of Digital Libraries10.1007/978-3-642-33290-6_41(383-395)Online publication date: 23-Sep-2012
https://dl.acm.org/doi/10.1007/978-3-642-33290-6_41

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