Search

Background: Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework. Methods: By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs). Results: IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements. Conclusion: Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models. [ABSTRACT FROM AUTHOR]

Abstract: Blogs have become an important social tool. It allows the users to share their tastes, express their opinions, report news, form groups related to some subject, among others. The information obtained from the blogosphere may be used to create several applications in various fields. However, due to the growing number of blogs posted every day, as well as the dynamicity of the blogosphere, the task of extracting relevant information from the blogs has become difficult and time consuming. In this paper, we use information retrieval and extraction techniques to deal with this problem. Furthermore, as blogs have many variation points is required to provide applications that can be easily adapted. Faced with this scenario, the work proposes RetriBlog, an architecture-centered framework for the development of blog crawlers. Finally, it presents an evaluation of the proposed algorithms and three case studies. [Copyright &y& Elsevier]

Paraphrase identification consists in the process of verifying if two sentences are semantically equivalent or not. It is applied in many natural language tasks, such as text summarization, information retrieval, text categorization, and machine translation. In general, methods for assessing paraphrase identification perform three steps. First, they represent sentences as vectors using bag of words or syntactic information of the words present the sentence. Next, this representation is used to measure different similarities between two sentences. In the third step, these similarities are given as input to a machine learning algorithm that classifies these two sentences as paraphrase or not. However, two important problems in the area of paraphrase identification are not handled: (i) the meaning problem: two sentences sharing the same meaning, composed of different words; and (ii) the word order problem: the order of the words in the sentences may change the meaning of the text. This paper proposes a paraphrase identification system that represents each pair of sentence as a combination of different similarity measures. These measures extract lexical, syntactic and semantic components of the sentences encompassed in a graph. The proposed method was benchmarked using the Microsoft Paraphrase Corpus, which is the publicly available standard dataset for the task. Different machine learning algorithms were applied to classify a sentence pair as paraphrase or not. The results show that the proposed method outperforms state-of-the-art systems. [ABSTRACT FROM AUTHOR]