Composición de la dieta y estrategia alimentaria de cinco especies de peces bentónicos de la costa de Cullera (España)

This paper provides data on diet and feeding habits of five benthic fish species (Torpedo torpedo (Linnaeus,1758), Mullus surmuletus (Linnaeus, 1758), Uranoscopus scaber (Linnaeus,1758), Scorpaena scrofa (Linnaeus,1758) and Synaptura lusitanica (Capello,1868)) common in the artisanal fisheries in the Cullera coast (Mediterranean sea – Spain) and T.torpedo, U.scaber y S. lusitánica feeding habits are almost unknown. T. torpedo preferred small preys like fishes, polychaetes and molluscs, these preys were feed in small portions. M. surmuletus showed the highest feeding dynamic, consuming small prey in large numbers like crustaceans (brachyura and amphypoda). U. scaber had similar feeding habits, but the numbers of preys in the stomach were lower. The principal preys were fishes, crustaceans, molluscs and polychaetes. S.scrofa ate larger prey items such as fish, followed by crustaceans and molluscs. Finally S. lusitánica had a high vacuity index, feeding polychaetes as the most important prey in their diet. Feeding strategy indicates a specialization of T.torpedo, S.scrofa and S. lusitanica; conversely M. surmuletus and U. scaber were generalized species.

Using WordNet relations and semantic classes in information retrieval tasks

In this paper we explore the use of semantic classes in an existing information retrieval system in order to improve its results. Thus, we use two different ontologies of semantic classes (WordNet domain and Basic Level Concepts) in order to re-rank the retrieved documents and obtain better recall and precision. Finally, we implement a new method for weighting the expanded terms taking into account the weights of the original query terms and their relations in WordNet with respect to the new ones (which have demonstrated to improve the results). The evaluation of these approaches was carried out in the CLEF Robust-WSD Task, obtaining an improvement of 1.8% in GMAP for the semantic classes approach and 10% in MAP employing the WordNet term weighting approach.