Discovering taxonomies in Wikipedia by means of grammatical evolution

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  author =       "Lourdes Araujo and Juan Martinez-Romo and 
                 Andres Duque Fernandez",
  title =        "Discovering taxonomies in {Wikipedia} by means of
                 grammatical evolution",
  journal =      "Soft Computing",
  year =         "2018",
  volume =       "22",
  number =       "9",
  pages =        "2907--2919",
  keywords =     "genetic algorithms, genetic programming, grammatical
  URL =          "",
  timestamp =    "Sat, 05 May 2018 23:05:31 +0200",
  biburl =       "",
  DOI =          "doi:10.1007/s00500-017-2544-4",
  abstract =     "This work applies grammatical evolution to identify
                 taxonomic hierarchies of concepts from Wikipedia. Each
                 article in Wikipedia covers a topic and is cross-linked
                 by hyperlinks that connect related topics. Hierarchical
                 taxonomies and their generalization to ontologies are a
                 highly useful resource for many applications since they
                 enable semantic search and reasoning. Thus, the
                 automatic identification of taxonomies composed of
                 concepts associated with linked Wikipedia pages has
                 attracted much attention. We have developed a system
                 which arranges a set of Wikipedia concepts into a
                 taxonomy. This technique is based on the relationships
                 among a set of features extracted from the contents of
                 the Wikipedia pages. We have used a grammatical
                 evolution algorithm to discover the best way of
                 combining the considered features in an explicit
                 function. Candidate functions are evaluated by applying
                 a genetic algorithm to approximate the optimal taxonomy
                 that the function can provide for a number of training
                 cases. The fitness is computed as an average of the
                 precision obtained by comparing, for the set of
                 training cases, the taxonomy provided by the evaluated
                 function with the reference one. Experimental results
                 show that the proposal is able to provide valuable
                 functions to find high-quality taxonomies.",

Genetic Programming entries for Lourdes Araujo Juan Martinez-Romo Andres Duque Fernandez