Journal of Chemical Information & Computer Sciences
Extraction and search of chemical formulae in text documents on the web
Proceedings of the 16th international conference on World Wide Web
ChemXSeer: a digital library and data repository for chemical kinetics
Proceedings of the ACM first workshop on CyberInfrastructure: information management in eScience
Mining, indexing, and searching for textual chemical molecule information on the web
Proceedings of the 17th international conference on World Wide Web
Exposing the hidden web for chemical digital libraries
Proceedings of the 10th annual joint conference on Digital libraries
High-Throughput identification of chemistry in life science texts
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
Catching the drift --- indexing implicit knowledge in chemical digital libraries
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
Hi-index | 0.00 |
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.