SimPack Crack+ Activation Code Free [32|64bit] [April-2022] ==================== SimPack is a Java library for the development of similarity measures, which is accessible from any programming language. The main focus of SimPack is on conceptual similarity. Therefor the algorithms implemented focus on the similarity of concepts. SimPack contains a functionality to test the similarity of concepts for different types of knowledge bases. This particular feature enables the users to test the usefulness of the similarity measure for different types of knowledge bases, and to adapt it in the required way. SimPack can also be used to test similarity of ontologies. In this case the algorithms implemented in SimPack support hierarchical structures and the similarity between ontologies. In addition to the algorithms SimPack offers a graphical user interface (GUI) that provides an easy way to visualise the similarity results in form of similarity networks and concept maps. The SimPack program is a component of the AxiGraP project. AxiGraP: The AxiGraph Project ============================= The AxiGraP project focuses on the development and evaluation of graph-based similarity measures. The main goal of the project is the support of the development and evaluation of similarity measures. The project is split into several modules. The graphical user interface (GUI) is an essential part of all project modules and is also the topic of this part. At the moment the GUI is implemented only as a set of Java Swing components. SimPack: Simplifying Concept Similarity Measures ================================================ SimPack is a small library for concept similarity measures that are based on the notion of similarity between concepts. SimPack offers a comprehensive set of algorithms for similarity measures and comparison of ontologies and concepts. With SimPack the complexity of similarity measures is reduced and the programming effort is minimized. SimPack is especially developed for the research of conceptual similarity between concepts. However, since SimPack is a modular library, it also offers functionality for conceptual similarity measures. Data files containing ontologies and concepts are tested with SimPack. After some modifications of the algorithms the data sets are used to simulate different types of semantic relationships. The algorithms implemented by SimPack make use of different similarity measures such as the Dice Similarity Index, the Jaccard Similarity Index, and the Normalized Information Similarity Index. SimPack is an important and necessary component of the AxiGraP project, which focuses on the development and evaluation of graph-based similarity SimPack SimPack is especially developed for the research of similarity between concepts in ontologies or ontologies as a whole. The SimPack approach is based on dynamic, multi-resolution concept hierarchies. This means that similarities are calculated between specific concepts only, but not between the concepts at a higher resolution. By this, the computation time is reduced. Moreover, SimPack allows to define and set the similarity thresholds and to weight similarities at a higher level. The following figure shows the basic idea of SimPack. You can use SimPack for individual concepts as well as for entire ontologies. Using SimPack for individual concepts: An input ontology or ontology as a whole is read and imported into SimPack. Similarities between concepts are calculated by using a similarity measure, which is defined by the user in a configuration file. The similarity values are transformed into ranges, which are based on the predefined similarity threshold. The highest similarity values can be set to infinity. The relevant similarities can be displayed in the SimPack user interface. The configuration file is a plain text file which contains the line separated value pairs and a line to define the output format. The file has to be saved as configuration.txt Ontology or ontology as a whole: An ontology or ontology as a whole is read by SimPack. Similarities between concepts are calculated. The highest similarity values are set to infinity. The highest similarities can be displayed in the SimPack user interface. Displaying SimPack: SimPack runs on a Windows desktop platform and provides a graphical user interface. SimPack provides several configuration options to adjust the output, such as selecting concepts for which similarity is displayed, the visualization mode and other criteria. Additionally, SimPack offers the possibility to customize the output or to configure individual output files by means of a configuration file. The most important options in SimPack are: Configuration: Definition of concepts and similarity measures. Threshold to display certain values: This defines the range in which the similarity values are displayed. Remaining similarity values: This shows the actual highest similarity values in the case that some similarities have been set to infinity. Color mapping of the similarity values: This defines the color mapping of the similarity values. Format of the output: Mapping of the similarity values: This defines the mapping of the similarity values. Column 77a5ca646e SimPack Crack + [32|64bit] SimPack is developed and maintained by the Ontology-Based Knowledge Engineering group of ETH Zurich. It is available for free under the GNU general public license. Predikam is a program for the predication extraction from PDF documents. It uses the PDF specification and the output of OpenOffice. The set of predicates is extracted from all the pages of the document, regardless of which output format the user has chosen, and regardless of whether the user has a pdf viewer installed. The extraction is done in four steps: 1. identification of what needs to be extracted 2. predication extraction 3. normalization 4. report generation Predikam is a Perl script. A description of the process, with a tutorial on how to use it, is available at Diffan is an open source engine for performing ontology-based comparisons of XML documents. It consists of three parts: a module for representing both XML documents and ontologies, a module for computing and evaluating similarity of ontologies, and an application for comparing XML documents and generating the results. Diffan can be used in batch mode, or on-line, with a GUI. Diffan was conceived at the Swiss Institute of Bioinformatics, and developed at University of Zurich, in Switzerland. An open-source implementation of the system is available at OIDEA is a tool for the extraction of all the UML diagrams stored in an XML schema. It is an application of the XML/OWL language which defines XML Schema as an ontology. It is released under the GNU General Public License (GPL). Indri is a tool for performing ontology-based comparisons of XML documents. It consists of four parts: a module for representing both XML documents and ontologies, a module for computing and evaluating similarity of ontologies, a module for comparing XML documents, and an application for generating the results. The extracted semantic contents of both documents are stored in a relational database. Indri can be used in batch mode, or on-line, with a GUI. Information Extraction is the automatic extraction of structured information from textual documents. It includes the extraction of named entities, their syntactic and semantic relationships and the extraction of structured data. Most of the algorithms are based on a Natural Language Processing approach. P What's New in the SimPack? ======================================= The OBO Relation Ontology (RO) is a formal representation of a set of relations that can hold between two ontologies. The relations between classes (A-Class and B-Class) in an ontology can be considered similar to a relationship. For example the relationship between cell and tissue in GO. The level of similarity between two classes can be measured by the type of the relations. For instance, the relation be_a and part_of are of type basic (for cell and tissue) while generic_part_of and is_a are sub-type specific relations (for tissue and cell). Similarity of two ontologies (A and B) is measured by counting the number of class pairs that are identical in both ontologies. The identity of two classes can be checked by using the OWL ontology language. We introduce a special relation to measure similarity between a complete ontology and the parts of the ontology. For example, the complete ontology can be represented by a larger ontology such as GO. Then this special relation can be used to measure similarity of GO to other ontologies. Similarly the sub-ontologies can be used to compare sub-parts of an ontology with each other. When considering a set of ontologies, we assume that the ontologies represent different information sources and thus have different levels of granularity. The granularity of an ontology can be captured by the composition of the ontology. A very coarse ontology would only represent the top level of the ontology tree. For example, GO at the first level would only have one class, while SNOMED CT would have a few thousand classes. If the granularity is very fine, then we can have thousands of classes in one ontology. The detailed classes in a coarse ontology might be too complicated for comparison with the classes of a fine ontology. Therefore, we recommend to use a coarse ontology for comparing a fine ontology or ontologies and vice versa. For example, a full GO is recommended for comparing sub-ontologies of GO or the domain ontology of an expert. The current version of SimPack is highly optimized for comparing ontologies with the same number of classes. But, we plan to extend the SimPack to compare ontologies of different sizes. ======================================= Comparing classes ============================ SimPack supports both exact matching and approximate matching of classes. Exact matching means that all attributes of a class are equal to the specified values. For example, we can specify a class in an ontology that is identical to the existing class or has all the attributes exactly equal to a certain value. Approximate matching means that we want to compare classes, where attributes of the System Requirements: OS: Windows 7 Windows 7 Processor: Intel Core i3/AMD Phenom II x 4 Intel Core i3/AMD Phenom II x 4 Memory: 2 GB RAM 2 GB RAM Graphics: NVIDIA GeForce 7800GT 512MB, AMD Radeon HD 3870 1024MB NVIDIA GeForce 7800GT 512MB, AMD Radeon HD 3870 1024MB DirectX: DirectX 10 DirectX: DirectX 11 DirectX: DirectX 12 Release Date: TBA TBA Price: TBA
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