DistAD: A Distributed Generic Anomaly Detection Framework over Large KGs

DistAD: A Distributed Generic Anomaly Detection Framework over Large KGs

This Release includes the recent developments for the DistAD framework.
DistAD is the Anomaly Detection framework for large KGs.

Overview

This module is a generic, scalable, and distributed framework for anomaly detection on large RDF knowledge graphs. DistAD provides a great granularity for the end-users to select from a vast number of different algorithms, methods, and (hyper-)parameters to detect outliers. The framework performs anomaly detection by extracting semantic features from entities for calculating similarity, applying clustering on the entities, and running multiple anomaly detection algorithms to detect the outliers on the different levels and granularity. The output of DistAD will be the list of anomalous RDF triples.

Documents

An explained tutorial and full documention can be found here and here.

Resource

We provide the full jar of this version below