LOD2 Creating Knowledge out of Interlinked Data

The DL-Learner software learns concepts in Description Logics (DLs) from examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. It extends Inductive Logic Programming to Descriptions Logics and the Semantic Web. The goal of DL-Learner is to provide a DL/OWL based machine learning tool to solve supervised learning tasks and support knowledge engineers in constructing knowledge and learning about the data they created.

Purposes of Class Expression Learning:

1. Learn Definitions for Classes: Based on existing instances of an OWL class, DL-Learner can make suggestions for class definitions to be included as an owl:equivalentClass or rdfs:subClassOf Axiom. As the algorithm is biased towards short and human readable definitions, a knowledge engineer can be supported when editing the TBox of an ontology (see Protege Plugin).
2. Find similar instances: DL-Learner's suggested class expressions can be used to find similar instances via retrieval (Concept definitions as search). Scalable methods allow the generation of recommendations on the fly, e.g. in a web scenario (see DBpedia Navigator - in experimental stage).
3. Classify instances: The learned class descriptions can be used in a typical classification scenario, i.e. to decide for unknown instances whether they belong to a certain class. Common ILP benchmarks have been tested with DL-Learner. On the Carcinogenesis page, DL-Learner competes with other state-of-the-art ILP algorithms.