Jose Maria Gonzalez Pinto
Wolf-Tilo Balke
ABSTRACT
Efforts to make highly specialized knowledge accessible through scientific digital libraries need to go beyond mere bibliographic metadata, since here information search is mostly entity-centric. Previous work has realized this trend and developed different methods to recognize and (to some degree even automatically) annotate several important types of entities: genes and proteins, chemical structures and molecules, or drug names to name but a few. Moreover, such entities are often crossreferenced with entries in curated databases. However, several questions still remain to be answered: Given a scientific discipline what are the important entities? How can they be automatically identified? Are really all of them relevant, i.e. do all of them carry deeper semantics for assessing a publication? How can they be represented, described, and subsequently annotated? How can they be used for search tasks? In this work we focus on answering some of these questions. We claim that to bring the use of scientific digital libraries to the next level we must find treat topic-specific entities as first class citizens and deeply integrate their semantics into the search process. To support this we propose a novel probabilistic approach that not only successfully provides a solution to the integration problem, but also demonstrates how to leverage the knowledge encoded in entities and provide insights to explore the use of our approach in different scenarios. Finally, we show how our results can benefit information providers.
- D. M. Blei, A. Y. NG, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research. 2003 Google Scholar
Digital Library
- Blei, D. M., & Lafferty, J. D. (2009). Topic Models. In Text Mining: Classification, Clustering, and Applications (pp. 71--89). Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. doi:10.1145/1143844.1143859Google Scholar
- Blei, D. M. (2012). Introduction to Probabilistic Topic Modeling. Communications of the ACM, 55, 77--84. doi:10.1145/2133806.2133826. Google ScholarDigital Library
- Goulart, R. R. V., Strube de Lima, V. L., & Xavier, C. C. (2011). A systematic review of named entity recognition in biomedical texts. Journal of the Brazilian Computer Society. doi:10.1007/s13173-011-0031--9.Google Scholar
- Settles, B. (2005). ABNER: An open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics, 21, 3191--3192. doi:10.1093/bioinformatics/bti475 Google ScholarDigital Library
- Filippov, I. V., & Nicklaus, M. C. (2009). Optical structure recognition software to recover chemical information: OSRA, an open source solution. Journal of Chemical Information and Modeling, 49, 740--743. doi:10.1021/ci800067rGoogle ScholarCross Ref
- Lowe, D. M., Corbett, P. T., Murray-Rust, P., & Glen, R. C. 2011. Journal of Chemical Information and Modeling, 51, 739--753. doi:10.1021/ci100384dGoogle Scholar
- Park, J., Rosania, G. R., Shedden, K. A., Nguyen, M., Lyu, N., & Saitou, K. (2009). Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal, 3, 4. doi:10.1186/1752--153X-3--4Google ScholarCross Ref
- P. Sojka and M. Lška. The Art of Mathematics Retrieval. Proceedings of the ACM Conference on Document Engineering. 2011 Google ScholarDigital Library
- Michael Kohlhase, Bogdan A. Matican, and Corneliu C. Prodescu. MathWebSearch 0.5 -Scaling an open Formula Sarch Engine. Conferences on Intelligent Computer Mathematics (CICM). 2012 Google ScholarDigital Library
- Kamali, S., & Tompa, F. W. (2013). Retrieving documents with mathematicalcontent. In Proceedings of the 36th international ACM SIGIRconference on Research and development in information retrieval -- SIGIR '13 (p. 353). doi:10.1145/2484028.2484083 Google ScholarDigital Library
- Sun, B., Mitra, P., & Giles, C. L. (2008). Mining, indexing, and searching for textual chemical molecule information on the web. In Proceeding of the international conference on World Wide Web (pp. 735--744). doi:10.1145/1367497.1367597 Google ScholarDigital Library
- Tönnies, S., Köhncke, B., Koepler, O., & Balke, W.-T. (2010). Exposing the Hidden Web for Chemical Digital Libraries. In Int.l Joint Conference on Digital Libraries (pp. 234--244). doi:10.1145/1816123.1816159 Google ScholarDigital Library
- Vickrey, D., Biewald, L., Teyssier, M., & Koller, D. (2005). Word-Sense Disambiguation for Machine Translation. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT '05) (pp. 771--778). doi:10.3115/1220575.1220672 Google ScholarDigital Library
- Carpuat, M., & Wu, D. (2007). Improving statistical machine translation using word sense disambiguation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 61--72. Retrieved from papers2://publication/uuid/CA8E0BC3--96B6--4123--8674--4E4BD98AACA9Google Scholar
- Brody, S., & Lapata, M. (2009). Bayesian Word Sense Induction. Computational Linguistics, 103--111. doi:10.3115/1609067.1609078 Google ScholarDigital Library
- Lau, J. H., Cook, P., McCarthy, D., Newman, D., Baldwin, T., & Computing, L. (2012). Word sense induction for novel sense detection. In Proceedings of the 13th Conference of the European Chapter of the Association for computational Linguistics (EACL 2012) (pp. 591--601). Google ScholarDigital Library
- Firth, J. R. (1957). A synopsis of linguistic theory 1930--55. Studies in Linguistic Analysis (special Volume of the Philological Society), 1952--59, 1--32.Google Scholar
- Griffith TL, Steyvers M (2004). Finding Scientic Topics. Proceedings of the National Academy of Sciences of the United States of America, 101, 5228--5235Google ScholarCross Ref
- Burges, C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2, 121--167. Retrieved from /papers/Burges98.ps.gz Google ScholarDigital Library
- Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical Dirichlet Processes. Journal of the American Statistical Association. doi:10.1198/016214506000000302Google Scholar
Index Terms
- Demystifying the Semantics of Relevant Objects in Scholarly Collections: A Probabilistic Approach
Recommendations
Russian Scholarly Papers in Open-Access Megajournals
AbstractThe quantity, research topics, and growth rates are assessed for Russian scholarly papers published in open-access megajournals. Russian papers published in PLoS ONE in 2006–2019 are analyzed on the basis of international scientometric indicators. ...
Scholarly publications beyond pay-walls: increased citation advantage for open publishing
First, we aim to determine the total amount of scholarly articles freely available on the internet. Second, we aim to prove whether there exists a citation advantage for open publishing. The total scholarly publication output of Norway is indexed in ...
Disciplinary differences in Twitter scholarly communication
This paper investigates disciplinary differences in how researchers use the microblogging site Twitter. Tweets from selected researchers in ten disciplines (astrophysics, biochemistry, digital humanities, economics, history of science, cheminformatics, ...
Comments