-
Dextre Clarke, S.G.: Challenges and opportunities for KOS standards (2007)
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- Date
- 22. 9.2007 15:41:14
-
Hofmann-Apitius, M.: Direct use of information extraction from scientific text for modeling and simulation in the life sciences (2009)
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- Abstract
- Scientific biomedical publications are a rich source of information about diseases and the molecules that play a role in the molecular etiology of a disease. With the development of automated methods for the identification of named biomedical entities in scientific text ("text mining") we are now able to automatically screen millions of publications for genes, their relationships to other genes, their role in the development of a disease and their role as potential targets for therapeutic cures. In fact, modern advanced search engines are now able to extract various terms in scientific text that represent entities which can be directly used for modeling of diseases and simulation of disease-relevant molecular networks. In my presentation, I will demonstrate how scientific text can be analyzed using a combination of algorithmic approaches (dictionary- and rule-based as well as machine learning - based methods). I will furthermore demonstrate, how scientific information extracted from text can be applied in disease modeling approaches that combine heterogeneous information types (protein-protein-interactions, allelic variants of genes, clinical phenotype information) extracted from scientific publications. I will furthermore show how the analysis of scientific text can be used to construct "knowledge descriptors" that allow a completely new way of predicting the activity of small pharmaceutical molecules. Taken together, the talk will hopefully provide a clue how far we really are from using text analytics for direct modeling and simulation in the life sciences.
-
Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008)
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- Theme
- Data Mining
-
Mai, F.; Galke, L.; Scherp, A.: Using deep learning for title-based semantic subject indexing to reach competitive performance to full-text (2018)
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- Abstract
- For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.9%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.
-
Strobel, S.: ¬The complete Linux kit : fully configured LINUX system kernel (1997)
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- Date
- 16. 7.2002 20:22:55
-
Birmingham, J.: Internet search engines (1996)
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- Date
- 10.11.1996 16:36:22
-
Zumer, M.; Clavel, G.: EDLproject : one more step towards the European digtial library (2007)
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- Content
- Vortrag anläasslich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009)
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- Date
- 1. 3.2013 14:56:22
-
Qin, J.; Paling, S.: Converting a controlled vocabulary into an ontology : the case of GEM (2001)
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- Date
- 24. 8.2005 19:20:22
-
Broughton, V.: Automatic metadata generation : Digital resource description without human intervention (2007)
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- Date
- 22. 9.2007 15:41:14
-
Tudhope, D.: Knowledge Organization System Services : brief review of NKOS activities and possibility of KOS registries (2007)
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- Date
- 22. 9.2007 15:41:14
-
Isaac, A.: After EDLproject : controlled Vocabularies in TELPlus (2007)
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- Content
- Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Dini, L.: CACAO : multilingual access to bibliographic records (2007)
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- Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Leth, P.: Subject access - the Swedish approach (2007)
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- Content
- Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Karg, H.: Mapping Dewey and subject authorities : CrissCross (2007)
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- Vortrag anläasslich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Hengel, C.: Mapping name authorities : the Virtual International Authority File (VIAF) (2007)
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- Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Goossens, P.: Authority control : trends and challenges (2007)
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- Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Radhakrishnan, A.: Swoogle : an engine for the Semantic Web (2007)
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- Content
- "Swoogle, the Semantic web search engine, is a research project carried out by the ebiquity research group in the Computer Science and Electrical Engineering Department at the University of Maryland. It's an engine tailored towards finding documents on the semantic web. The whole research paper is available here. Semantic web is touted as the next generation of online content representation where the web documents are represented in a language that is not only easy for humans but is machine readable (easing the integration of data as never thought possible) as well. And the main elements of the semantic web include data model description formats such as Resource Description Framework (RDF), a variety of data interchange formats (e.g. RDF/XML, Turtle, N-Triples), and notations such as RDF Schema (RDFS), the Web Ontology Language (OWL), all of which are intended to provide a formal description of concepts, terms, and relationships within a given knowledge domain (Wikipedia). And Swoogle is an attempt to mine and index this new set of web documents. The engine performs crawling of semantic documents like most web search engines and the search is available as web service too. The engine is primarily written in Java with the PHP used for the front-end and MySQL for database. Swoogle is capable of searching over 10,000 ontologies and indexes more that 1.3 million web documents. It also computes the importance of a Semantic Web document. The techniques used for indexing are the more google-type page ranking and also mining the documents for inter-relationships that are the basis for the semantic web. For more information on how the RDF framework can be used to relate documents, read the link here. Being a research project, and with a non-commercial motive, there is not much hype around Swoogle. However, the approach to indexing of Semantic web documents is an approach that most engines will have to take at some point of time. When the Internet debuted, there were no specific engines available for indexing or searching. The Search domain only picked up as more and more content became available. One fundamental question that I've always wondered about it is - provided that the search engines return very relevant results for a query - how to ascertain that the documents are indeed the most relevant ones available. There is always an inherent delay in indexing of document. Its here that the new semantic documents search engines can close delay. Experimenting with the concept of Search in the semantic web can only bore well for the future of search technology."
-
Surfing versus Drilling for knowledge in science : When should you use your computer? When should you use your brain? (2018)
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- Editorial: Surfing versus Drilling for Knowledge in Science: When should you use your computer? When should you use your brain? Blaise Pascal: Les deux infinis - The two infinities / Philippe Hünenberger and Oliver Renn - "Surfing" vs. "drilling" in the modern scientific world / Antonio Loprieno - Of millimeter paper and machine learning / Philippe Hünenberger - From one to many, from breadth to depth - industrializing research / Janne Soetbeer - "Deep drilling" requires "surfing" / Gerd Folkers and Laura Folkers - Surfing vs. drilling in science: A delicate balance / Alzbeta Kubincová - Digital trends in academia - for the sake of critical thinking or comfort? / Leif-Thore Deck - I diagnose, therefore I am a Doctor? Will drilling computer software replace human doctors in the future? / Yi Zheng - Surfing versus drilling in fundamental research / Wilfred van Gunsteren - Using brain vs. brute force in computational studies of biological systems / Arieh Warshel - Laboratory literature boards in the digital age / Jeffrey Bode - Research strategies in computational chemistry / Sereina Riniker - Surfing on the hype waves or drilling deep for knowledge? A perspective from industry / Nadine Schneider and Nikolaus Stiefl - The use and purpose of articles and scientists / Philip Mark Lund - Can you look at papers like artwork? / Oliver Renn - Dynamite fishing in the data swamp / Frank Perabo 34 Streetlights, augmented intelligence, and information discovery / Jeffrey Saffer and Vicki Burnett - "Yes Dave. Happy to do that for you." Why AI, machine learning, and blockchain will lead to deeper "drilling" / Michiel Kolman and Sjors de Heuvel - Trends in scientific document search ( Stefan Geißler - Power tools for text mining / Jane Reed 42 Publishing and patenting: Navigating the differences to ensure search success / Paul Peters
-
Guidi, F.; Sacerdoti Coen, C.: ¬A survey on retrieval of mathematical knowledge (2015)
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- Date
- 22. 2.2017 12:51:57
