We have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.

The MdM Strategic Research Program has its own community in Zenodo for material available in this repository  as well as at the UPF e-repository  . Below a non-exhaustive list of datasets representative of the research in the Department.

As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)

 

 

Back Dr. Inventor Text Mining Framework

Dr. Inventor Text Mining Framework is a Java library that integrates several Document Engeneering and Natural Language Processing tools customized to enable and ease the analysis of the textual contents of scientific publications.


Dr. Inventor Text Mining Framework is a standalone Java library that enable users to process the contents of papers both in PDF and JATS XML format. Once imported a paper from a local file or a remote URL, the Framework automatically extracts and characterizes several aspects including:

  • Structural elements: title, abstract, hierarchy of sections, sentences inside each section, bibliographic entries
  • Bibliographic entries are parsed and enriched by accessing external web services (Bibsonomy, CrossRef, FreeCite, Google Scholar)
  • Inline citations are spotted and linked to the respective bibliographic entry
  • The dependency tree is built from each sentence by considering inline citations
  • The discoursive category of each sentence is identified among: Background, Challenge, Approach, Outcome and Future Work
  • BabelNet synsets are spotted inside the contents of each sentence thanks to Babelfy
  • Subject-Verb-Object graphs are build to represent the contents of paper excerpts (the connectedness of these graphs is enhanced thanks to coreference resolution)
  • Relevant sentences are selected with respect to several criteria to build extractive summaries of a paper
  • etc.

Ronzano, F., & Saggion, H.: Dr. Inventor Framework: Extracting Structured Information from Scientific  Publications. Discovery Science (pp. 209-220). Springer International Publishing. (2015)

Link: http://backingdata.org/dri/library/

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