SimPack is specially developed for the research of similarity between concepts in ontologies or ontologies as a whole.
Possible other application areas of SimPack include
· the investigation of similarity between software source code. For instance to detect changes between classes of different software releases.
· the research of similarity between hierarchically-structured data, such as XML, to compare, search, or integrate data from different data sources.

SimPack is an open-source package of Java libraries for computing the semantic similarity and closeness among a set of concepts. SimPack provides not only the core similarity algorithm, but also an efficient implementation. By applying SimPack, your project won’t need to write any code to compute similarity between concepts. All the work will be done by your computer.
SimPack implements all algorithms provided by the original paper of Jakob et al. (2008) [1] using an incremental neural network framework with an equivalent discrete implementation.
SimPack also supports multiple source concept representation formats:
· OWL, with DL, DL-Lite and EL signatures.
· OBO ontologies.
· REL ontologies.
· Large number of concept identifiers, including UUIDs (Universally Unique IDentifier), URIs (Uniform Resource Identifiers), XML names, and OBO IDs.
· Subsumption.
· Hierarchical data.
In addition, SimPack supports the following additional input and output formats:
· XML document.
· CDT (Closest Concept Distance Table) file.
· XSD (XML Schema Definition) document.
The user can also easily extend SimPack by adding new models to the automatic network, or combining the trained model with the new input format.
SimPack is implemented in Java using JavaCC. JRuby is used for the JRubyJ-based and Python-based extensions.
SimPack Features:
· Multithreading.
· Tiling.
· Significance testing (using p-values from Mann-Whitney).
· Hierarchical input format.
· Hierarchical output format.
· OBO semantics.
· REL semantics.
· OWL semantics.
· XML semantics.
· Large concept identifiers.
· Subsumption support.
· Hierarchical data.
· UUIDs.
· URIs.
· XML names.
· OBO IDs.
· CDT and XSD output.
· Output formats supported.
· Option to save the trained weights.
· Import a trained model from the user’s source model.
· Export a trained model as Java files.
· Custom weight format.
· Very fast computation.
· Very efficient storage.
· Good hardware scalability.
· Binary version.
· Support on all Java VM versions.
· Support on all Java JRE

· Has been written using Java.
· The code contains mainly procedural functions, which provide easy to learn.
· The source code has been developed in such a way that it is easy to add other functionality to the package, such as functions to calculate the similarity between 2 ontologies. These additional functionalities can easily be added by changing the.java source files.
· Class Hierarchy contains similarity between concepts.
· In class Hierarchy concepts can be moved to the right places according to their similarity.
· Up to 2 hierarchies can be open simultaneously.
· Open classes can be closed by putting the corresponding arrows on their left hand side.
· Open classes can be compared with the concept of the parent class by putting the corresponding arrows on the left hand side of the left class and the right class.
· The concept of a single class may be extended by clicking on the plus sign in the concept editor.
· The concept of the parent class can be removed by pressing the “minus” button.
· Object Hierarchy.
· Concept Hierarchy has facilities similar to those of the class hierarchy.
· Object Hierarchy has facilities similar to those of the concept hierarchy.
· Object Hierarchy has facilities similar to those of the class hierarchy.
· Object Hierarchy supports the comparison between objects.
· In the concept editor you can see all super classes and super classes of a concept as well as the children.
· There is a hierarchical concept show concept in the concept editor.
· There is a hierarchical concept show concepts in the concept editor.
· There is a hierarchical concept show objects in the concept editor.
· Concept editors and the concept editor window can be scrolled.
· Configurations are saved in the workspace.

Synchronize Frequentisk.

This project (Synchronize Frequentisk) delivers an application that is designed for frequent interactions (synchronizing) of all frequentisk information saved in an XML file. Special attention is given to its usability. It was developed for employees or others working with frequentisk. The project includes source code and a documentation.

InfoWorkPlus.

InfoWorkPlus.

InfoWorkPlus

The project InfoWorkPlus is build on the top of a user interface developed for the needs of frequentisk-employed parties as well as administrators of frequentisk and companies that use frequentisk.

The InfoWorkPlus user interface is not to be seen as an alternative to the “classic”
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SimPack is developed for the comparison between concepts in ontologies or ontologies as a whole. It includes a function to find similarity among concepts of a given domain. The output of SimPack includes individual Sim-values representing the concept similarity to each compared concept.

Behavioral Health Cluster (BHC) was designed to evaluate the use of automated, real-time evaluation of behavioral health outcomes (e.g., anxiety, depression, aggression, substance use, etc.), as a means of providing feedback on intervention progress to the care team.

The evaluation targeted three populations: adolescent inpatients (N=42) and adolescent outpatients (N=35) presenting to a large urban, public hospital Child and Adolescent Emergency Psychiatry (CAP) clinic and an urban, public high school clinic (N=33). The IntelliCare™ software (based on the Infotel™) was used to measure anxiety, depression, aggression, substance use, and self-injurious and suicidal ideation at three specific time points in both adolescents’ and parents’ perspectives. One of the goals of this study was to determine the feasibility of using this type of assessment to evaluate the progress of treatment, as well as to inform the design of a parent-report outcome measure.

Notably, 66% of the adolescent sample was available for post-treatment comparisons, and 64% of the adolescent sample was available for follow up post-treatment. These results demonstrate the feasibility of using an automated, real-time approach to patient tracking. Moreover, the feasibility of using an automated, real-time approach to patient tracking suggested that the IntelliCare™ software may provide valuable information about patient progress to inform the design of a potentially stronger parent-report outcome measure.

The project generated results that support the feasibility of evaluating parental responses to automated electronic tracking of behavioral health data collected in adolescent CAP settings. However, the availability of parent-reports could also result in increased participant burden for both parents and adolescents. The focus of future research is to test the reliability of parent-report outcome measures in the context of electronic tracking.

This study provides an example of a successful implementation that results in high patient retention rates. The long term sustainability of this project is being determined by evaluating the impact of the electronic tracking on treatment outcomes of CAP patients. Preliminary results suggest that treatment outcomes are impacted by the use of electronic tracking.

This study provides an example of a successful implementation that results in high patient retention rates. The

SimPack is especially developed to compute
the similarity of ontology terms,
to compare ontologies, ontology parts, or
ontologies as a whole.
An ontology is a structured, explicit and formal knowledge representation. An ontology contains a set of concepts, each represented by a class or a type, represented by
a relation or a sub-class, and defined by a set of axioms. More complex ontologies have properties, which are defined by sub-properties.
SimPack is based on the assumption that ontologies are parts of a larger structure that models the world. A model of the world is similar to the world and it is possible to compute similarities between it.
SimPack solves this problem with the help of efficient techniques, which calculate similarity
between different values.
This is done by matching values, common patterns, and sets. As an example, when
two sets are compared, the elements of one set are matched with the elements of the other set. If one set has items that are not contained in the other set, it means that the items are dissimilar.
SimPack can be used for any size of ontologies as it only has dependencies on the size of the input values.
SimPack is also able to calculate the similarity of non-classified concepts, i.e., that are not associated with any concept, or to calculate the similarity of the whole ontology.
One of the interesting application areas of SimPack is the investigation of software source code similarity. For instance to detect changes between classes of different software releases.
The program can be used for all kinds of ontologies and configurations.
SimPack has the following interface:

Input :

– set of ontology concepts;
– set of ontology parts;
– ontology (all concepts, parts, etc);
– ontology parts to analyze as of input;
– a configuration file, specifying the similarity method to use;
– input values.

Output :

– similarity of all input values;
– similarity between pairs of input values.

To calculate the similarity of all input values, SimPack needs the number of ontology concepts in the input and the number of ontology parts in the input.
The complexity of calculation is proportional to the size of the input values. SimPack starts by calculating the similarity of all input values to find out the highest similarity. The similarity of each pair of input values is then calculated.
The input values

Minimum:
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