Blog entries

  • DBpedia 3.2 released

    2008/11/19 by Nicolas Chauvat
    http://wiki.dbpedia.org/images/dbpedia_logo.png

    For those interested in the Semantic Web as much as we are at Logilab, the announce of the new DBpedia release is very good news. Version 3.2 is extracted from the October 2008 Wikipedia dumps and provides three mayor improvements: the DBpedia Schema which is a restricted vocabulary extracted from the Wikipedia infoboxes ; RDF links from DBpedia to Freebase, the open-license database providing about a million of things from various domains ; cleaner abstracts without the traces of Wikipedia markup that made them difficult to reuse.

    DBpedia can be downloaded, queried with SPARQL or linked to via the Linked Data interface. See the about page for details.

    It is important to note that ontologies are usually more of a common language for data exchange, meant for broad re-use, which means that they can not enforce too many restrictions. On the opposite, database schemas are more restrictive and allow for more interesting inferences. For example, a database schema may enforce that the Publisher of a Document is a Person, whereas a more general ontology will have to allow for Publisher to be a Person or a Company.

    DBpedia provides its schema and moves forward by adding a mapping from that schema to actual ontologies like UMBEL, OpenCyc and Yago. This enables DBpedia users to infer from facts fetched from different databases, like DBpedia + Freebase + OpenCyc. Moreover 'checking' DBpedia's data against ontologies will help detect mistakes or weirdnesses in Wikipedia's pages. For example, if data extracted from Wikipedia's infoboxes states that "Paris was_born_in New_York", reasoning and consistency checking tools will be able to point out that a person may be born in a city, but not a city, hence the above fact is probably an error and should be reviewed.

    With CubicWeb, one can easily define a schema specific to his domain, then quickly set up a web application and easily publish the content of its database as RDF for a known ontology. In other words, CubicWeb makes almost no difference between a web application and a database accessible thru the web.


  • Nous allons à PyConFr 2009

    2009/05/25 by Arthur Lutz

    Le 30 et 31 mai prochain (samedi et dimanche prochain) nous allons être présents à PyConFr édition 2009, nous sommes partenaire de l'évènement et allons y parler de CubicWeb. Pour être plus précis, Nicolas Chauvat y présentera "CubicWeb pour publier DBpedia et OpenLibrary". Il avait déjà évoqué ces sujets sur ce site : Fetching book descriptions and covers et DBpedia 3.2 released.

    Si vous comptez y aller, n'hésitez pas à venir nous dire bonjour.

    http://pycon.fr/images/logo_pyconfr_small.png

  • Rss feeds aggregator based on Scikits.learn and CubicWeb

    2011/10/17 by Vincent Michel

    During Euroscipy, the Logilab Team presented an original approach for querying news using semantic information: "Rss feeds aggregator based on Scikits.learn and CubicWeb" by Vincent Michel This work is based on two major pieces of software:

    http://www.cubicweb.org/data/index-cubicweb.png
    • CubicWeb, the pythonic semantic web framework, is used to store and query Dbpedia information. CubicWeb is able to reconstruct links from rdf/nt files, and can easily execute complex queries in a database with more than 8 millions entities and 75 millions links when using a PostgreSQL backend.
    http://scipy-lectures.github.com/_images/scikit-learn-logo.png
    • Scikit.learn is a cutting-edge python toolbox for machine learning. It provides algorithms that are simple and easy to use.
    http://www.pfeifermachinery.com/img/rss.png

    Based on these tools, we built a pure Python application to query the news:

    • Named Entities are extracted from RSS articles of a few mainstream English newspapers (New York Times, Reuteurs, BBC News, etc.), for each group of words in an article, we check if a Dbpedia entry has the same label. If so, we create a semantic link between the article and the Dbpedia entry.
    • An occurrence matrix of "RSS Articles" times "Named Entities" is constructed and may be used against several machine learning algorithms (MeanShift algorithm, Hierachical Clustering) in order to provide original and informative views of recent events.
    http://wiki.dbpedia.org/images/dbpedia_logo.png

    Moreover, queries may be used jointly with semantic information from Dbpedia:

    • All musical artists in the news:

      DISTINCT Any E, R WHERE E appears_in_rss R, E has_type T, T label "musical artist"
      
    • All living office holder persons in the news:

      DISTINCT Any E WHERE E appears_in_rss R, E has_type T, T label "office holder", E has_subject C, C label "Living people"
      
    • All news that talk about Barack Obama and any scientist:

      DISTINCT Any R WHERE E1 label "Barack Obama", E1 appears_in_rss R, E2 appears_in_rss R, E2 has_type T, T label "scientist"
      
    • All news that talk about a drug:

      Any X, R WHERE X appears_in_rss R, X has_type T, T label "drug"
      

    Such a tool may be used for informetrics and news analysis. Feel free to download the complete slides of the presentation.