Open Source Mac Artificial Intelligence Software

The CDM-Core ontology (CREMA Data Model - core module) is a manufacturing ontology, specialised in the two application sub-domains of 1) exhaust car manufacturing and 2) metallic press maintenance The CDM-Core ontology was developed by: Dr. Luca Mazzola, Msc. Patrick Kapahnke, Marko Vujic, and PD Dr. Matthias Klusch at the German Research Center for Artificial Intelligence DFKI GmbH (http://www.dfki.de ) in Saarbrücken, Germany. Copyright: DFKI, 2016, licence CC BY-SA see http://creativecommons.org/licenses/by-sa/3.0/ For bug reports, technical problems and feature requests please contact: Luca Mazzola: luca.mazzola@dfki.de or mazzola.luca@gmail.com Patrick Kapahnke: patrick.kapahnke@dfki.de

This is a database of the Arabic roots and their derivatives in voweled and unvoweled forms along with stems. The database is extracted from the well known Arabic legacy dictionary "تاج العروس من جواهر القاموس".

MOVED TO https://github.com/acoli-repo/olia. The Ontologies of Linguistic Annotations (OLiA) provide an OWL/DL taxonomy of data categories as a reference for linguistic annotation (OLiA Reference Model), plus OWL/DL models for a large number of annotation schemes (OLiA Annotation Models) and their relationship to reference data categories (OLiA Linking Models). The OLiA Reference Model itself is linked to community-maintained repositories such as GOLD (http://linguistics-ontology.org/) and ISOcat (http://www.isocat.org) The OLiA ontologies were originally developed as part of an infrastructure for the sustainable maintenance of linguistic resources (http://www.sfb441.uni-tuebingen.de/c2/index-engl.html), their fields of application include the formalization of annotation schemes, concept-based querying over heterogeneously annotated corpora, and the development of interoperable NLP pipelines.

Die BOS-Ontologie ist eine formale Domänen- Ontologie der deutschen BOS (Behörden und Organisationen mit Sicherheitsaufgaben). Sie stellt eine terminologische Grundlage einer beschreibungslogischen Wissensbasis dar, anhand derer logische Schlussfolgerungsmechanismen eine intelligente Auswertung von Lageinformationen ermöglichen sollen.

Hermes is a repository of software, documentation and data for NLP. I am currently adding corpora extracted from Wikipedia (mostrly in Romance languages).

WARNING: There are too many false-positives! This is Alpha release, expect many things to improve, including the algorithms. PLEASE GO TO BROWSE ALL FILES TO READ A FULL DESCRIPTION. The goal of this project is simple: Input two sentences of the same language, and obtain the number (from 0 to 1) denoting the similarity between the inputted sentences, according to semantic categories. This project models my previous project: https://sourceforge.net/projects/semantics/ Difference is, this project does not use any database and computes any Strings as an input. JAVA was the language of choice, due to availability of modelling tools. This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/. -Powered by WEKA, Classifier4J and SimMetrics.

This program is made to address two most common issues with the known classifying algorithms. First, over-training and second, shortage of data for a training of categories. Instead, each TXT file is a category on its own, rather than an assigned category. In a way, this is similar to clustering but not really a clustering algorithm since there is some training involved. The summarizer from Classifier4J has been adjusted to accept two inputs (lets call them A and B). Then, the summarizer gets trained with A to summarize a document B, and vice versa. This extracts a relevant structure for both documents (and thus avoids the over-training) which are then compared using the Vector-Space analysis to give a range of belonging of one document to another (and thus avoids the shortage of information). This method can be used to create the user-defined classes by merging texts of certain categories and then to calculate the relevant distances between the documents, but this is not necessary.

The BOT is used in our Discord server.