Cognition-based approaches for high-precision text mining

This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in. Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital and connected world, cognitive approaches enable substantial opportunities in knowledge discovery. The generation and storage of electronic text is ubiquitous, hence opportunities for improved knowledge discovery span virtually all knowledge domains. While cognition-based search offers superior approaches, challenges exist due to the need to mimic, even in the most rudimentary way, the extraordinary powers of human cognition. This research addresses these challenges in the key area of a cognition-based approach to automated concept recognition. In addition it resulted in a semantic processing system framework for use in applications in any knowledge domain. Confabulation theory was applied to the problem of automated concept recognition. This is a relatively new theory of cognition using a non-Bayesian measure, called cogency, for predicting the results of human cognition. An innovative distance measure derived from cogent confabulation and called inverse cogency, to rank order candidate concepts during the recognition process. When used with a multilayer perceptron, it improved the precision of concept recognition by 5% over published benchmarks. Additional precision improvements are anticipated. These research steps build a foundation for cognition-based, high-precision text mining. Long-term it is anticipated that this foundation enables a cognitive-based approach to automated ontology learning. Such automated ontology learning will mimic human language cognition, and will, in turn, enable the practical use of cognitive-based approaches in virtually any knowledge domain --Abstract, page iii

Folkoncept: método de suporte à modelagem conceitual de ontologias a partir da aquisição de conhecimentos de folksonomias

In this work, we present a method called Folkoncept for supporting conceptual modeling of ontologies starting with knowledge acquisition based on folksonomies. The method aims at helping actors enrolled in the development process in eliciting terms and in the modeling choice of how to represent these terms in the ontology. The objective of applying the Folkoncept method is to reduce the knowledge acquisition bottleneck through ontology learning techniques based on folksonomies. Folkoncept reaches three activities of the development process: knowledge acquisition, conceptual modeling, and evaluation, the latter being integrated into the conceptual modeling activity. With relation to the knowledge acquisition, Folkoncept deals with the retrieval, representation, and enrichment of terms (tags) coming from a folksonomy resulting from a social process of tagging performed by the actors involved in the ontology development process. In the conceptual modeling activity, Folkoncept helps the ontology designer to transform folksonomy’s tags into elements of the ontology being developed. In the ontology evaluation activity, the method helps ontology designers to validate the new elements that are suggested by the ontology learning method. In addition, the Folkoncept reduces the difficulty in using the OntoClean methodology making its use transparent to the ontology designer. Folkoncept was evaluated by means of ontology development experiments realized in a controlled environment by teams composed by ontology designers coming from the area of computing. Some teams worked with a prototype system that implements the Folkoncept. Results obtained by these teams were compared with the results from teams working without the system. The comparison was performed through metrics that show that the Folkoncept helped ontology designers to develop more descriptive ontologies with fewer errors with relation to the idealized taxonomies of OntoClean.Neste trabalho, apresenta-se um método para o desenvolvimento de ontologias a partir de folksonomias. O objetivo do método é auxiliar os atores envolvidos no processo de desenvolvimento na elicitação de termos a serem representados na ontologia e na tomada de decisão de como modelar tais termos. Busca-se, pela aplicação do método, reduzir o gargalo na aquisição de conhecimentos empregando-se técnicas de aprendizado de ontologias a partir de folksonomias. O método atinge três atividades do processo de desenvolvimento de ontologias: aquisição de conhecimentos, modelagem conceitual e avaliação das ontologias, sendo este último integrado à modelagem conceitual. Na aquisição de conhecimentos, o método trata da recuperação, representação e enriquecimento das etiquetas (termos) presentes nas folksonomias originadas de um processo social de etiquetagem realizado pelos atores envolvidos no desenvolvimento da ontologia. Na modelagem conceitual, auxilia o projetista a transformar as etiquetas das folksonomias em elementos da ontologia em desenvolvimento, ou seja, na modelagem de novos elementos. Na avaliação de ontologias, o método auxilia os projetistas na validação dos novos elementos que são sugeridos pelo método de aprendizado. Além disso, o método diminui a dificuldade em utilizar a metodologia OntoClean tornando sua aplicação transparente ao projetista. A avaliação do método foi realizada por meio de experimentos de desenvolvimento de ontologias em um ambiente controlado. Participaram dos experimentos equipes compostas por projetistas da área da computação, sendo que algumas equipes trabalharam com um protótipo que implementa o método e outras não. A avaliação foi realizada por meio de métricas que comprovaram que o sistema auxiliou os projetistas a desenvolverem ontologias mais descritivas e com número menor de erros de formalismo