DL-Learner – Framework para Aprendizado de Máquina

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.

Example

  1. Instance Classification: A user maintains a list of favorites. Based on these favorites, OWL Concepts are learned with DL-Learner and presented to the user in Natural Language. Such a concept could be all articles about proteins that are written by researchers from Germany (e.g. in Manchester syntax: Proteins and hasAuthor some (Person and hasLocation some Germany)). New articles, which fall in this category and are added to the knowledge base, are presented to the user automatically, like a customized RSS feed.
  2. Protégé: In a family ontology, a Protégé user wants to create a definition for the Concept ‘Father’. He / She already asserted some instances to the class Father. Now, the DL-Learner Protege plugin presents the definition (in Manchester OWL syntax): Male and hasChild some Thing.

Implementation

The application is written in Java. A user manual can be found PDF Documenthere. Also there is an overview, a page about its architecture, and a feature list Features. DL-Learner is available as Open-Source at Sourceforge.

It has different learning algorithms, which offer several parameters for fine-tuning. It can solve four closely related learning problems: learning based on positive and negative examples, positive only learning, and learning definitions and subclass relationships in ontologies.

Scalability

As reasoning is heavily used by DL-Learners algorithms, special methods were introduced to increase performance:

  1. Fast Instance Checker is a reasoning component, that is custom tailored for the needs of DL-Learner. After an initial reasoning step on the basis of Pellet, results are pre-calculated and cached. Besides the significant performance boost, the component can optionally apply a form of closed world reasoning, which allows to learn expressions like forall and max/min cardinality. It is an approximate reasoning method, where as usual rare cases of incomplete reasoning results are justified by a huge increase in performance.
  2. DL-Learner can also provide class suggestions for very large knowledge bases, since it uses local fragment reasoning, i.e. only the relevant part (which is small) is used for learning new classes. This enables class learning in real time on knowledge bases like DBpedia. More information can be found PDF Documenthere.
DL-Learner – Framework para Aprendizado de Máquina

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