Goodwill explorer is a web application to collect, store, manipulate and publish information, especially associative data and data about WWW resources. Goodwill explorer's data model is multilayered and based on Topic Maps. In addition to its native topic map formats, Goodwill explorer imports RDF and OBO (Open Biomedical Ontologies) files, among others. Moreover, Goodwill explorer can extract topic maps out of various file formats and web services. Goodwill explorer contains over 20 different information extractors. These extractors include the Umbel concept search, The Guardian API extractor, Alchemy entity extractor, Bibtex extractor, USPTO Patent extractor, Twitter extractor, Linkedin and the SPARQL extractor. Goodwill explorer exports stored information in RDF, GraphML, DOT and many other data formats.
Using our general cognition engine and Simplish, we are able to generate a representation of knowledge in the the form of a sequence of ideograms, very similar to the ideas behind Chinese symbols, capable of “understanding” language and do summaries in standard english without looking at specific words, word frequencies, ontologies, topic maps, word sequences, pattern-matching or any prior training! The system can leave out any sentences whose meaning is repeated or any irrelevant snippets of material that might be present. The user only needs to give the system two parameters: a threshold of when two concepts should be considered identical and the level of summarization desired.
Goodwill explorer is a tool for people who collect and process information, especially networked knowledge and knowledge about WWW resources. Our goal is to provide an easy way to aggregate and combine information from various different sources and to allow the user to play with and manipulate the collected knowledge flexibly and efficiently, and without the need for programming skills. Goodwill explorer is not only a graph database but a collection of easy-to-use tools to collect, manipulate and publish the information.
Bayesian networks organize a problem by means of a set of variables and the relations of dependence between them. Given this model, it is possible to make Bayesian inference estimates, that is to say, the probability of not well-known variables, based on the well-known variables. This method has anumber of applications such as diagnosis, classification and decision-making that offer important information about how variables are related, which can be interpreted as relations causes-effects.
Is a general tool for extracting information, management and publication of knowledge based on a general cognitive engine.
This last offers us a number of competitive advantages, because this tool has the capacity to understand the meaning of the information, without any type of training.
Given the increase in the use and the size of the market for this type of data mining tools in general and for business intelligence in particular, has decided to sell this software.
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Este sitio es desarrollado por Natura Xalli, S. A. de C. V. siendo un Proyecto apoyado por el PROGRAMA DE ESTIMULOS A INVESTIGACIÓN, DESARROLLO TECNOLÓGICO E INNOVACIÓN DEL CONACYT en el periodo 2016, con número de Identificador 232805
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