Download JAR files tagged by exploiting with all dependencies

WebGraph is a framework to study the web graph. It provides simple ways to manage very large graph, exploiting modern compression techniques. The big version is a fork of the original WebGraph that can handle more than 2^31 nodes.

The HBase server module (ks-server-hbase) provides an implementation of the Data Store internal sub-component of the Knowledgestore based on the Apache HBase no-sql store, exploiting OMID and Apache Avro to support, respectively, operation transactionality and serialization of efficient serialization of structured data in byte strings.

Persistence Annotation for RDF (PAR) is a set of annotations and an entity manager that provides JPA like functionality on top of an RDF store while accounting for and exploiting the fundamental differences between graph storage and relational storage. PAR introduces three (3) annotations that map a RDF triple (subject, predicate, object) to a Plain Old Java Object (POJO) using Java's dynamic proxy capabilities.

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (e.g. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (e.g. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html