/PRNewswire/ -- Everlaw, the cloud-native investigation and litigation platform, unveiled its Clustering software feature today, delivering an AI breakthrough...
Command line:
```bash
jupyter nbconvert --to latex --TagRemovePreprocessor.remove_cell_tags='{"skip"}' --TagRemovePreprocessor.enabled=True 'nb.ipynb'
```
Also, to use it via python you need to enable the `TagRemoveProcessor` manually.
See: [source](https://stackoverflow.com/q/58564376/991496)
%A Author
%B Secondary Title (of a Book or Conference Name)
%C Place Published
%D Year
%E Editor /Secondary Author
%F Label
%G Language
%H Translated Author
%I Publisher
%J Journal Name
%K Keywords
%L Call Number
%M Accession Number
%N Number (Issue)
%O Alternate Title
%P Pages
%Q Translated Title
%R DOI
%S Tertiary Title
%T Title
%U URL
%V Volume
%W Database Provider
%X Abstract
%Y Tertiary Author / Translator
%Z Notes
%0 Reference Type
%1 Custom 1
%2 Custom 2
%3 Custom 3
%4 Custom 4
%6 Number of Volumes
%7 Edition
%8 Date
%9 Type of Work
%? Subsidiary Author
%@ ISBN/ISSN
%! Short Title
%# Custom 5
%$ Custom 6
%] Custom 7
%& Section
%( Original Publication
%) Reprint Edition
%* Reviewed Item
%+ Author Address
%^ Caption
%> File Attachments
%< Research Notes
%[ Access Date
%= Custom 8
%~ Name of Database
As it is often the case for social software services, online reference managers are becoming powerful and costless solutions to collect large sets of metadata, in this case collaborative metadata on scientific literature.
Bildveröffentlichung auf der Karte, wo es genau entstanden ist, sprich Land, Stadt, Strasse und wer bzw. was auf dem Foto zu sehen ist angeben. Verlinkung zu Mahara
The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows the full materialization of the Semantic Web, as these systems are cheap, extendable, scalable and respond quickly to user needs. However, for the sake of knowledge workflow, one needs to find a compromise between the ungoverned nature of folksonomies and the controlled vocabulary of domain-experts. In this paper, we address this concern by first devising a method that automatically combines folksonomies with domain-expert ontologies resulting in an enriched folksonomy. We then introduce a new algorithm based on frequent itemsets mining that efficiently learns an ontology over the concepts present in the enriched folksonomy. Moreover, we propose a new benchmark for ontology evaluation, which is used in the context of information finding, since this is one of the leading motivations for using ontologies in social tagging systems, to quantitatively assess our method. We conduct experiments on real data and empirically show the effectiveness of our approach.
Abstract. Web-based tagging systems for educational resources allow users
to associate free keywords with resources to facilitate their retrieval and
reuse. This paper looks at the similarities and differences among three
different systems. We first focus on the purpose of tagging and the
incentives for users to tag educational resources. Then, we compare the
most used tags in each system. We find that even if the tagging system
design decisions differ, there is a number of similarities in tags that are
shared among more than one of the services. Moreover, our goal is to
discuss the reuse of tags across these systems and use them as a
navigational aid for a user to cross system boundaries.
Absolutely one of the best organizational methods I have seen in a while Density is huge. Usability is amazingly good. Also content is amazing with great links behind it.