Abstract. Linked Data brings the promise of incorporating a new dimension to the Web where the availability of Web-scale data can determine a paradigmatic transformation of the Web and its applications. However, together with its opportunities, Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked Data on the Web, or on corporate intranets, should be able to search and query data spread over potentially a large number of heterogeneous, complex and distributed datasets. Ideally, a query mechanism for Linked Data should abstract users from the representation of data. This work focuses on the investigation of a vocabulary independent natural language query mechanism for Linked Data, using an approach based on the combination of entity search, a Wikipediabased semantic relatedness measure and spreading activation. The combination of these three elements in a query mechanism for Linked Data is a new contribution in the space. Wikipedia-based relatedness measures address existing limitations of existing works which are based on similarity measures/term expansion based on WordNet. Experimental results using the query mechanism to answer 50 natural language queries over DBPedia achieved a mean reciprocal rank of 61.4%, an average precision of 48.7% and average recall of 57.2%, answering 70% of the queries.

Tasks such as question answering and semantic search are dependent on the ability of querying & reasoning over large-scale commonsense knowledge bases (KBs). However, dealing with commonsense data demands coping with problems such as the increase in schema complexity, semantic inconsistency, incompleteness and scalability. This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model which can be applied to querying & reasoning over heterogeneous knowledge bases (KBs). The approach can be used for approximative reasoning, querying and associational knowledge discovery. In this paper we focus on commonsense reasoning as the main motivational scenario for the approach. The approach focuses on addressing the following problems: (i) providing a semantic selection mechanism for facts which are relevant and meaningful in a specific reasoning & querying context and (ii) allowing coping with information incompleteness in large KBs. The approach is evaluated using ConceptNet as a commonsense KB, and achieved high selectivity, high scalability and high accuracy in the selection of meaningful navigational paths. Distributional semantics is also used as a principled mechanism to cope with information incompleteness.

A associação das helicobacterias com doença gástrica em humanos e em alguns animais domésticos e selvagens sugere a participação dessas na patogênese da gastrite em cães. Neste artigo procurou-se verificar a presença de Helicobacter spp. na mucosa gástrica de cães e avaliar sua associação com os achados macro e microscópicos, considerando a idade. Coletaram-se amostras das regiões cárdica, fúndica, do corpo e pilórica dos estômagos de 60 cães para a realização de exame histopatológico, utilizando-se as colorações pela hematoxilina-eosina (HE) e carbolfucsina (CF). Tais exames revelaram Helicobacter spp. em 96,7% dos animais, sendo observados infiltrados inflamatórios, predominantemente mononucleares (100%), hiperplasia de nódulos linfoides (98,3%), erosões/ulcerações (6,7%), hemorragia (5%) e hiperemia (95%) em amostras coradas por HE. Não houve correlação da infecção por helicobacter spp. com a idade do animal e da idade com alterações inflamatórias na presença da bactéria. As amostras das regiões do corpo e pilórica apresentaram maior presença de bactérias a histopatologia (CF) (ambos 95%), em seguida vindo as regiões fúndica (91,7%) e cárdica (56,7%). Houve correlação do número de Helicobacter spp. com o de células inflamatórias e nódulos linfoides nessas regiões, sugerindo que as bactérias encontradas na mucosa gástrica dos cães poderiam ser responsáveis pelas alterações que caracterizam a gastrite.

Abstract. Linked Data promises an unprecedented availability of data on the Web. However, this vision comes together with the associated challenges of querying highly heterogeneous and distributed data. In order to query Linked Data on the Web today, end-users need to be aware of which datasets potentially contain the data and the data model behind these datasets. This query paradigm, deeply attached to the traditional perspective of structured queries over databases, does not suit the heterogeneity and scale of the Web, where it is impractical for data consumers to have an a priori understanding of the structure and location of available datasets. This work describes Treo, a best-effort natural language query mechanism for Linked Data, which focuses on the problem of bridging the semantic gap between end-user natural language queries and Linked Datasets.

RESUMO O objetivo deste estudo foi avaliar a frequência de lesões nas diferentes regiões do estômago de equinos assintomáticos. Foi realizada avaliação macroscópica dos

Rodentes captured in a veterinary hospital and a fragment of atlantic forest in Zona da

Abstract. Natural language descriptors used for categorizations are present from folksonomies to ontologies. While some descriptors are composed of simple expressions, other descriptors have complex compositional patterns (e.g. 'French Senators Of The Second Empire', 'Churches Destroyed In The Great Fire Of London And Not Rebuilt'). As conceptual models get more complex and decentralized, more content is transferred to unstructured natural language descriptors, increasing the terminological variation, reducing the conceptual integration and the structure level of the model. This work describes a representation for complex natural language category descriptors (NLCDs). In the representation, complex categories are decomposed into a graph of primitive concepts, supporting their interlinking and semantic interpretation. A category extractor is built and the quality of its extraction under the proposed representation model is evaluated.

Veterinário autônomo Resumo O presente estudo teve por objetivo correlacionar o número de bactérias espiraladas e as alterações histológicas da mucosa gástrica em cães de vida livre. Foram analisadas biopsias gástricas endoscópicas de 28 cães assintomáticos. Para análise histológica, foi realizada avaliação qualitativa, onde foram atribuídos escores de 0 a 3, considerando a densidade de bactérias espiraladas por campo (400x), a presença de células inflamatórias, o número de agregados linfoides e a existência de alteração degenerativa glandular. A prevalência de Helicobacter spp, identificado pela histologia (Carbol-Fucscina) e positividade no teste da urease, foi de 100%. Dos 28 cães, 18 (64,3%) receberam escore 3 e 10 (35,7%) o escore 2 para a densidade de bactérias. O infiltrado inflamatório predominantemente linfoplasmocitário foi de grau leve (escore 1) em 17 (60,7%) cães e moderado em 6 (21,4%) cães. Dos 28 cães, 14 (50%) receberam escore 1 para degeneração glandular e 9 (32,1%), o escore 0. As regiões do corpo e antro apresentaram maior número de resultados positivos à histopatologia. Apesar do número elevado de bactérias encontrado nas amostras analisadas, as alterações histológicas foram classificadas como de grau leve na maioria dos animais. A presença do Helicobacter spp. não parece estar relacionado com sintomatologia de gastrite.