• Corpus ID: 17343048

ScienceWISE: Topic Modeling over Scientific Literature Networks

@article{Martini2016ScienceWISETM,
  title={ScienceWISE: Topic Modeling over Scientific Literature Networks},
  author={Andrea Martini and Artem Lutov and Valerio Gemmetto and Andrii Magalich and Alessio Cardillo and Alexandru Constantin and Vasyl Palchykov and Mourad Khayati and Philippe Cudr{\'e}-Mauroux and Alexey Boyarsky and Oleg Ruchayskiy and Diego Garlaschelli and Paolo De Los Rios and Karl Aberer},
  journal={ArXiv},
  year={2016},
  volume={abs/1612.07636}
}
We provide an up-to-date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics. So far, SW has been proven to be an effective platform for managing large volumes of technical articles by means of ontological concept-based browsing. However, as the publication of research articles accelerates, the expressivity and the richness of the SW ontology turns into a double-edged sword: a more fine-grained… 

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References

SHOWING 1-10 OF 33 REFERENCES

Ground truth? Concept-based communities versus the external classification of physics manuscripts

TLDR
A detailed analysis of discrepancies shows that they may carry essential information about the system, mainly related to the use of similar techniques and methods across different (sub)disciplines, that is otherwise omitted when only the external classification is considered.

Extending the definition of modularity to directed graphs with overlapping communities

TLDR
This paper starts from the definition of a modularity function, given by Newman to evaluate the goodness of network community decompositions, and extends it to the more general case of directed graphs with overlapping community structures.

Automatic structure and keyphrase analysis of scientific publications

TLDR
The first part of this work details a novel method for recovering the rhetorical structure of scientific articles that is competitive with state-of-the-art machine learning techniques, yet requires no layout-specific tuning or prior training.

Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.

TLDR
The basic ideas behind the previous benchmark are extended to generate directed and weighted networks with built-in community structure, and the possibility that nodes belong to more communities is considered, a feature occurring in real systems, such as social networks.

Finding Statistically Significant Communities in Networks

TLDR
OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.

Finding and evaluating community structure in networks.

  • M. NewmanM. Girvan
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
TLDR
It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.

A new methodology for constructing a publication-level classification system of science

TLDR
This work introduces a new methodology for constructing classification systems at the level of individual publications, and presents an application in which a classification system is produced that includes almost 10 million publications.

Comparing network covers using mutual information

TLDR
This work extends the use of mutual information to covers, that is, the cases where a node can belong to more than one module, and defines the stochastic process that is not only applicable to networks, but can also be used to compare more general set-to-set binary relations.

Extracting the multiscale backbone of complex weighted networks

TLDR
A filtering method is defined that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistically significant deviations with respect to a null model for the local assignment of weights to edges.